Final Thesis

A thesis entitled
THE DETRIMENTAL IMPACT OF TURNOVERS IN THE DEFENSIVE HALF AS
IT PERTAINS TO WINS, LOSSES, AND DRAWS IN A SOCCER MATCH
Submitted to the Carroll University Library in
partial fulfillment of the expectations
and academic requirement of the
degree of Masters in Education
by
Matthew B. Drago
Research Facilitator, Dr. Sandra Shedivy Date
Program Chair, Dr. Wilma J. Robinson Date
Mentor, Miss Catherine M. Kaiser Date
Graduate Support Library Liason, Susan Hefferon Date

The Detrimental Impact of Turnovers in the Defensive Half as it Pertains to Wins,
Losses, and Draws in a Soccer Match
by
Matthew B. Drago
A thesis submitted in partial fulfillment
of the requirements for the degree of
Master of Education
at
Carroll University, Waukesha, Wisconsin
May 2012

iii
TABLE OF CONTENTS
Approval Page
Title Page
Table of Contents…..............................................................................................iii
Abstract…............................................................................................................. v
List of Tables….....................................................................................................vi
CHAPTER ONE: INTRODUCTION…................................................................... 1
Introduction….................................................................................................. 1
Research problem… ....................................................................................... 5
Purpose statement….. .................................................................................. 10
Significance of this study… ........................................................................... 10
Data collection…........................................................................................... 14
Data analysis…............................................................................................. 15
Research questions…................................................................................... 16
Definition of terms…...................................................................................... 17
Limitations… ................................................................................................. 18
Delimitations….............................................................................................. 18
Overview of chapters…................................................................................. 19
CHAPTER TWO: LITERATURE REVIEW…. ..................................................... 21
Introduction………………….. ........................................................................ 21
American football… ................................................................................. 22
Professional basketball…. ....................................................................... 26
College basketball…................................................................................ 27
Research regarding wins, losses, and draws in soccer…............................. 29
General soccer…..................................................................................... 29
Conclusion…................................................................................................. 38
CHAPTER THREE: METHODOLOGY
Introduction…................................................................................................ 40
Research design…........................................................................................ 42
Participants…................................................................................................ 44
NCAA division I athletics… ...................................................................... 44
Schools… ................................................................................................ 46
Bowling Green State University…....................................................... 46
Indiana University…............................................................................ 46
Marquette University… ....................................................................... 46
Michigan State University…................................................................ 47

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Northwestern University… .................................................................. 47
The Ohio State University… ............................................................... 47
Pennsylvania State University…......................................................... 48
University of Akron… .......................................................................... 48
University of Louisville…..................................................................... 48
University of Michigan… ..................................................................... 48
University of Wisconsin-Green Bay…................................................. 48
University of Wisconsin-Madison…..................................................... 49
University of Wisconsin-Milwaukee… ................................................. 49
Cases… ........................................................................................................ 49
Data collection…........................................................................................... 50
Data analysis…............................................................................................. 52
CHAPTER FOUR: RESULTS
Introduction…................................................................................................ 53
Data findings….............................................................................................. 54
Case 1: University of Wisconsin-Madison vs. University of Wisconsin
Green Bay…............................................................................................ 54
Case 2: University of Wisconsin-Green Bay vs. University of Wisconsin-
Milwaukee… ............................................................................................ 55
Case 3: University of Wisconsin-Madison vs. University of Michigan…... 57
Case 4: Bowling Green University vs. The Ohio State University… ........ 58
Case 5: Northwestern University vs. The Ohio State University…........... 59
Case 6: Northwestern University vs. Pennsylvania State University… .... 59
Case 7: Pennsylvania State University vs. Michigan State University….. 61
Case 8: University of Notre Dame vs. Marquette University…................. 61
Case 9: Indiana University vs. University of Michigan….......................... 62
Case 10: University of Akron vs. University of Michigan… ...................... 63
Summary of cases…..................................................................................... 65
CHAPTER FIVE: CONCLUSIONS….................................................................. 68
Introduction…................................................................................................ 68
Higher skill level has less correlation to turnovers in the defensive half… .... 69
Unexpected significance…............................................................................ 70
Implications…................................................................................................ 72
Implementations and recommendations….................................................... 73
REFERENCES…. .............................................................................................. 75

v
ABSTRACT
The Detrimental Impact of Turnovers in the Defensive Half as it Pertains to Wins,
Losses, and Draws in a Soccer Match
by
Matthew B. Drago
Carroll University, 2012
Under the Supervision of Dr. Sandra Shedivy, Research Facilitator

vi
LIST OF TABLES
Table 1: 2010 NBA Finals..................................................................................... 4
Table 2: Turnovers in the Defensive Half ........................................................... 14
Table 3: Chart for Keeping Statistics .................................................................. 15
Table 4: Probable Case/Events.......................................................................... 16
Table 5: 2006- 2010 Super Bowls ...................................................................... 23
Table 6: 2010 NCAA Tournament ...................................................................... 32
Table 7: Possession in the 2010 World Cup Final.............................................. 34
Table 8: 1991-2009 Women’s World Cup Statistics ........................................... 36
Table 9: 2010 World Cup Statistics .................................................................... 38
Table 10: Events Compared to Cases................................................................ 50
Table11: Madison Vs Green Gay ....................................................................... 55
Table 12: Green Bay Vs Milwaukee ................................................................... 56
Table 13: Madison Vs Michigan.......................................................................... 57
Table 14: Bowling Green Vs Ohio State ............................................................. 58
Table 15: Northwestern Vs Ohio State ............................................................... 59
Table 16: Northwestern Vs Penn State .............................................................. 60
Table 17: Penn State Vs Michigan State ............................................................ 61
Table 18: Notre Dame Vs Marquette.................................................................. 62
Table 19: Indiana Vs Michigan ........................................................................... 63
Table 20: Akron Vs Michigan.............................................................................. 64
Table 21: Cumulative Match Totals .................................................................... 65
Table 22: Statistics Averaged............................................................................. 66

1
Chapter One: Introduction
Introduction
Wins and losses are almost always the barometer used when judging a
team’s effectiveness. But is it the wins and losses that identify the team or the
components that went into building the team’s wins, losses, and draws that is
more important? Athletic competitions have nearly infinite reasons for why teams
win or lose: athletic ability, coaching, and practice hours to name a few. In recent
years, the idea of “10,000 hours” of practice has been noteworthy. Levitin (as
cited in Gladwell, 2008) discusses this phenomenon:
The emerging picture from such studies is that ten thousand hours of
practice is required to achieve the level of mastery associated with being a
world-class expert in anything. In study after study, of composers,
basketball players, fiction writers, ice skaters, concert pianists, chess
players, master criminals, and what have you, this number comes up
again and again. Of course, this doesn’t address why some people get
more out of their practice sessions than others do. But no one has yet
found a case in which true world-class expertise was accomplished in less
time. (p.40)
When studying professional and college level athletics, practice hours and the
quality of the practice exercises begin to take a backseat. At both the college and
professional levels, practice hours, practice quality, and natural ability seem to
move to the background while game tactics and schemes move to the forefront.
Research at these levels is rarely focused on practice quantity, quality, or talent,

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but rather the statistics associated with the actual games. In discussing athletic
events or contests, one must consider the simple possibility that one team’s
players are merely better than that of the other team. If one is to look a step
further at game statistics to better understand why teams are winning and losing
matches, one usually assumes the two teams competing are of similar talent.
This is generally accurate in professional and college athletics. Further, this
assumes that the amount of practice time and practice quality (intensity) for
players on both teams is similar with regard to the sport in which they are
participating. In this researcher’s opinion, wins, losses, and draws are no longer
a result of one team having more or less practice hours or quality (intensity) but,
rather opponent anticipation, strategy, and team cohesiveness may be much
more important.
Soccer is a sport that has been watched, studied, and dissected by fans,
coaches, and players for many years. Numerous studies have been conducted
which cover, in detail, the tangible statistics associated with soccer matches such
as: shots on goal, shots, corner kicks, goal kicks, free kicks, and in some cases,
consecutive passes. In looking at research conducted for other sports such as
football and basketball, the effects of turnovers on wins or losses have been
studied and discussed in depth. This researcher saw a gap in current and past
soccer research regarding lack of statistics pertaining to turnovers, that is, losing
control of the ball to the other team. The effect of turnovers and more specifically,
the effect of turnovers in the team’s defensive half in soccer, however, has not
been reported. It was this researcher’s goal in this study to study the effect of

3
turnovers in the defensive half of the field relative to the match outcomes.
In sports that are highly popular in the United States, turnovers and their
affect on the games’ outcome have been intensely covered and scrutinized. In
discussing the Packers and Steelers 2011 NFL Super Bowl, author Paul
Newberry (2011) of The Detroit News stated, “The Packers won [the turnover]
category going away. Therefore, they won the game” (p.1). Newberry
emphasized the importance of turnovers as it pertained to the outcome of this
specific football game. Later in Newberry’s article, he quoted Packers’ middle
linebacker Desmond Bishop, “If you win the turnover battle, there’s a direct
correlation to winning” (p.1). Further illustrating his point, Steeler running back,
Rashard Mendenhall was quoted as saying “When you turn the ball over like we
did, you put yourself in a bad position” (p.1).
A similar situation was noted by evaluating the statistics provided by
ESPN.com for the 2010 NBA Finals between the Los Angeles Lakers and the
Boston Celtics. This researcher identified the number of turnovers and rebounds
in the series and specifically looked to correlate the impact of turnovers and
rebounds to winning and losing. In the game of basketball both offensive and
defensive rebounds are similar to turnovers in soccer matches because they can
be considered a new possession for the rebounding (other) team. This
researcher collected and analyzed the data by adding team A's turnovers to team
B's rebounds, since both aspects illustrate positive outcomes for team B.

4
Likewise, team B's turnovers were added to team A's rebounds. Higher
totals equate to more possessions and in this particular best of seven series,
correlated with winning 86% of the time.
Table 1:
2010 NBA Finals Turnover Statistics
Forced
Turnovers
Defensive
Rebounds
Offensive
Rebounds
Total Score
Game 1
Lakers 13 30 12 55 102
Celtics 12 32 8 52 89
Game 2
Lakers 13 29 10 52 94
Celtics 15 31 13 59 103
Game 3
Lakers 10 32 11 53 91
Celtics 8 27 8 43 84
Game 4
Lakers 12 26 8 46 89
Celtics 15 25 16 56 96
Game 5
Lakers 16 18 16 50 86
Celtics 13 28 7 48 92
Game 6
Lakers 14 40 12 66 89
Celtics 13 28 11 52 67
Game 7
Lakers 14 30 23 67 83
Celtics 11 32 8 51 79
Totals
Lakers 92 205 92 389 634
Celtics 87 203 71 361 610
Taken from: http://www.espn.com
In every game except for game five, the team with more rebounds and
forced turnovers won the game. The total turnovers and rebounds for Game 5

5
were 50 and 48 for the Lakers and Celtics, respectively. It is significant to note
that with such a small difference (two), this was the only instance where the team
with the fewest turnovers and rebounds lost the game. This also correlates well
with the fact that the Lakers had twenty-eight more possessions and outscored
the Celtics by twenty-four points in the seven game series.
Although most dynamics of a soccer match have been studied in great
detail, the subject of turnovers and more importantly, turnovers in the team’s
defensive half, have been largely overlooked. By identifying the number of
turnovers in a team’s defensive half, it is this researcher’s opinion that coaches
and players can now use a new avenue of dissection for their own games with
regard to wins and losses. By identifying and correlating another aspect of play
in this research, and understanding the intricate points of soccer, it may enlighten
teams to take on new strategies that both force their team to create turnovers
against their opponents and possibly more importantly, place their more skilled
players in the back of their formation (defensive side) minimizing turnovers in
their own defensive halves.
Research problem
Presently statistics regarding turnovers goes largely uncollected by
statisticians in soccer matches, largely due to the fact that there is a large
number of instances where teams gain and lose possessions throughout a
soccer match. This can be attributed qualitatively to how one defines a turnover
as opposed to an organized attack that did not result with a goal. It also may be
attributed to an aggressively played ball in the attacking half that did not reach its

6
target. For the purposes of this study, the researcher limited the definition of a
turnover to these specific parameters:
a.) A ball passed to an intended receiver already on the defensive half of
the field at any distance that is intercepted by an opponent.
b.) A player losing the ball in the defensive half to an opponent while
attempting to dribble or hold the ball at their feet.
c.) A player improperly clearing the ball from his/her defensive half, in that
the ball was either completely missed or miss-hit such that the ball
stayed in the general area in which it was being cleared from.
d.) A goal keeper mishandling a catchable shot or cross and being
recovered by the opposing team.
Before a turnover can be identified, the team in the defensive half must have
clear control over the ball. Control of the ball is defined as; having the ball at the
player’s feet in a controlled roll or a complete stop for a period of one second or
longer. In this research, an uncontrollable bouncing ball was not considered to
be a possession. A ball that was cleared by the opposition, arriving with a 50%
chance of retrieval by the defensive team, known throughout this study as “50-50
balls,” was also not considered to be a turnover. It is important to note that throw-
ins were not considered turnovers at any point in this study as well. Pollard and
Reep (2007) define possession as follows:
A team possession starts when a player gains possession of the ball by
any means other than from a player of the same team. The player must
have enough control over the ball to be able to have a deliberate influence

7
on its subsequent direction. The team possession may continue with a
series of passes between players of the same team but ends immediately
when one of the following events occurs: a) the ball goes out of play; b)
the ball touches a player of the opposing team (e.g. by means of a tackle,
an intercepted pass or a shot being saved). A momentary touch that does
not significantly change the direction of the ball is excluded; c) an
infringement of the rules takes place (e.g. a player is offside or a foul is
committed). (p. 1)
This researcher decided to use Pollard and Reep’s definition to qualify and
quantify the data.
Soccer can be looked at through multiple statistical venues. In discussing
their study of game related statistics, Lago-Penas, Lago-Ballesteros, Dellal, and
Gomez (2010) stated:
When analyzing the results overall, the univariate analysis showed that
there are ten variables with statistically significant differences (total shots,
shots on goal, effectiveness, assists, crosses, crosses against, ball
possession, and red cards, and venue). On the other hand, when
applying a multivariate analysis, the number of statistically significant
variables was reduced to six (total shots, shots on goal, crosses, crosses
against, ball possession, and venue). (p. 291)
In data from the 2010-2011 European Premiere League provided by
premiereleague.com; Arsenal, Everton, Chelsea, and Manchester United led the
league in shots per game. Of these four teams, only Everton was not ranked in

8
the top five of the twenty team league. This fact notwithstanding, the three
remaining teams ranked (1) Manchester United, (2) Chelsea, and (3) Arsenal. In
simplistically looking at these statistics one may come to the conclusion that the
quantity of shots highly correlated with more wins. It is well accepted by soccer
professionals, that shots are generated through a multitude of outlets such as,
possession, fast breaks, lost tackles, fouls, and turnovers. One poorly timed
turnover can amount to a game losing goal where ten shots through routine, non-
scoring possessions may lead to nothing (no scores). It is also well known that
soccer is a game that needs to be evaluated over the course of a long season,
not individual matches.
Continuing with the multitude of reasons in which matches are won or lost,
Lago-Penas et al. (2010) further stated:
In the articles reviewed for the present study, there were no studies that
analyze the relationship between performance indicators related to
defence and team results. Probably, this gap is due to problems for
measuring these variables. Further research should address this topic. (p.
291)
By researching the number of turnovers in a team’s defensive half, coaches and
players have a new way in which to analyze their own games in addition to wins
and losses. By providing another facet of research and increasing one’s
understanding of the intricate, finer elements of soccer, it is this researcher’s
opinion that this will enlighten teams to take on new strategies that both force
their team to create turnovers against their opponents and possibly more

9
importantly, place their better skilled players in the back of their formation
allowing for fewer turnovers by their own team in their defensive half.
Other studies have been published with regard to statistics that are not
universally collected. Greevy, Germano, and Luyben (2009) found no statistical
significance in multiple sequenced passes from one game to the next. Greevy et
al. also saw a gap in the research of uncollected data in sequenced passing. In
an effort to close this gap they chose participants from a Division III college in
central New York State. A correct pass was defined as a pass in which:
1. The passer is looking at a teammate
2. The ball is directed to the teammate and not toward the goal (excludes
shots)
3. The teammate is in a position to trap and/or control the ball
4. The ball remains in bounds
5. The pass is not touched by an opposing player
The researchers studied the last eight game tapes over the course of the 2007
soccer season. The data were broken down between two halves, and
demonstrated more variability in the first half than in the second. Single passes
increased in the final three games of the season with two, three, and four pass
sequences ranging from 25%-35%, 10%-20% and 0%-10%, respectively of all
passes completed. Identifying increases or decreases of a team's passes
throughout the eight games, proved to have no statistical significance relative to
wins, losses, and draws. Tenga, Holmes, Tore, Ronglan, and Bahr (2010) stated
that the small sample size in the research was a limitation of the study. They

10
also noted this to be a problem with most soccer related research as well. This
researcher agrees with Greevy et al. (2009) that only reporting the results of
eight games, the conclusions may be suspect and not applicable across the
entire soccer spectrum.
In another study that correlated shots taken and games won, Lago-Penas
et al. (2010) compared their Spanish soccer league results to that of the 2002
World Cup and the Greek Soccer First League and found that the top teams
made more shots than bottom teams.
Purpose statement
The overall purpose of this study was to research a variable that has not
been discussed before (turnovers in the defensive half of the field) and evaluate
how these turnovers contribute to wins, losses, and draws in soccer matches.
In a game with so few statistics available to be followed, it was this
researcher’s opinion that soccer needed to collect more statistics, including the
more difficult (qualitative) statistics, such as turnovers in the defensive half.
Furthermore, if the consequences of turnovers in a team’s defensive half
becomes qualitatively significant, it may result in teams re-thinking their offensive
and defensive strategies relative to building their attacks further from their
defensive goal than was previously accepted.
Significance of the Study
As stated by Bourdieu, as cited in Christensen (2009):
Experts in a given activity such as soccer coaching are considered experts
because their flair for sensing what is going to happen-their “feel for the

11
game” is valued and is assigned capital in the field of soccer. Practical
sense here is not a result of logical thinking or declarative knowledge. It is
founded on practical intuition or habitus, which might be informed by
explicit knowledge, but is primarily based on hands-on and incorporated
knowhow earned through a legitimate and privileged access to the field.
(p. 368)
Regarding player selection, coaches are often making decisions that are largely
based on their “feel for the game,” when it could be a combination of feel and
statistics. The research compiled in this study attempted to close the gap
between qualitative “feel for the game” and quantitative game statistics.
The overall intent of this research project was to fill a perceived void in
statistical analysis of soccer matches. The researcher had seen a gap in major
sports in indicators statisticians viewed as prevalent; the researcher wanted to
close that gap and identify a previously unidentified statistic that could have a
major impact on soccer match results. The researcher noted that in several
major sports, including basketball, football, lacrosse, and rugby, turnovers were
identified and covered in great detail. The researcher has played soccer his
entire life and noted that soccer did not keep track of this statistic, possibly
because of the subjective (qualitative) nature in distinguishing turnovers from the
regular flow of play.
The overall intent of the study was to give another avenue for teams to
evaluate retrospectively relative to wins, losses and draws. The results of this
study could also give credence to the idea of putting a team’s better skilled and

12
athletic players on defense to protect against critical turnovers in the defensive
half.
Research design
The researcher’s goal was to provide data and conclusions on how to
develop teams in terms of player positional placement, attacking styles and
defensive formations. It was hypothesized that the more information a coach or
team has, the more likely teams will gain an advantage on the competition and
be more successful over the course of a season, or a multitude of seasons.
The researcher wanted to identify if there was a direct correlation to
increased number of turnovers in a team’s defensive half, shots surrendered, and
shots on goal surrendered, to losing matches. For this study, the researcher
narrowed the research to turnovers in the defensive half because, in his
experience, those were the plays that tended to result in increased offensive
opportunities and shots for the opposing team.
The researcher viewed ten different NCAA Division I men’s soccer games;
the games were chosen based on games which were being broadcast by The
Big Ten Network or WISN Milwaukee. The statistics were collected personally by
the researcher; all games took place during the fall soccer season of 2011. This
study followed a qualitative design, using correlational case study methods. Data
were obtained and collected using statistics drawn from ten games played by
fourteen NCAA Division I men’s soccer teams.
The researcher will use a case-ordered effects matrix to study the causes
of the wins and losses. As stated in Miles and Huberman (1994), “a case-

13
ordered effects matrix sorts the cases by degrees of the major cause being
studied, and shows the diverse effects for each case” (p. 209). Miles and
Huberman go on to say that “the focus is on outcomes, dependent variables” (p.
209). The researcher viewed ten games for fourteen different NCAA teams:
Bowling Green State University, Indiana University, Marquette University,
Michigan State University, Northwestern University, Notre Dame, The Ohio State
University, Pennsylvania State University, University of Akron, University of
Louisville, University of Michigan, University of Wisconsin-Green Bay, University
of Wisconsin-Madison, and University of Wisconsin-Milwaukee. In order to
reduce any potential bias regarding the researcher inflating the statistics by
choosing particular games that may skew the statistics to prove the hypothesis,
the researcher watched game tapes of teams that were broadcast on one of two
local networks: The Big Ten Network and WISN Milwaukee. At no point did the
researcher view the previously charted game statistics of the matches that were
viewed. All statistics were taken first hand by the researcher.
The research design for this study incorporated qualitative events.
Creswell (2008) defines qualitative research as an in-depth exploration of the
“event” of a bounded system which means it is separated out for research in
terms of time, place or some physical boundaries (p. 465). Whereas Creswell
also defined quantitative research as: "A type of educational research in which
the researcher decides what to study, asks specific, narrow questions, collects
numeric (numbered) data from participants, analyzes these numbers using
statistics, and conducts the inquiry in an unbiased, objective manner" (p. 46). The

14
researcher used qualitative research and looked at one event, turnovers in the
defensive half, as it related to wins, draws, and losses, across ten cases. Table 2
below, illustrates those cases.
Table 2:
Turnovers in the Defensive Half
Event Cases
Case 1: University of Wisconsin-
Madison vs. University of Wisconsin-
Green Bay
Case 2: University of Wisconsin-Green
Bay vs. University of Wisconsin-
Milwaukee
Madison vs. University of Michigan
Case 4: Bowling Green University vs.
The Ohio State University
Case 5: Northwestern University vs.
Pennsylvania State University
Case 7: Pennsylvania State University
vs. Michigan State University
Case 8: University of Notre Dame vs.
Marquette University
Case 9: Indiana University vs.
University of Michigan
Case 10: University of Akron vs.
The researcher studied how turnovers in one’s defensive half, shots, and
shots on goal reflected changes in wins, losses, and draws.
Data collection
In order to test the correlation between turnovers in the defensive half,
shots, and shots on goal with regard to wins, losses, and draws, the researcher
watched ten game tapes for 14 different NCAA teams: Bowling Green State

15
University, Indiana University, Marquette University, Michigan State University,
Northwestern University, Notre Dame, The Ohio State University, Pennsylvania
State University, University of Akron, University of Louisville, University of
Michigan, University of Wisconsin-Green Bay, University of Wisconsin-Madison,
and University of Wisconsin-Milwaukee. To ensure that the researcher did not
inflate any statistics by choosing particular games that could have skewed the
statistics to prove the hypothesis, the researcher watched game tapes of teams
that were broadcast on one of two local networks: The Big Ten Network and
WISN Milwaukee.
Tally marks were made for the two teams involved in the match using the
following chart:
Table 3:
Chart for Keeping Statistics
Turnovers in
Defensive
Half
Goals
Goals directly
off of a
defensive
Turnover
Win/Loss/Dra
w
Away Team
Home Team
The top teams were the visitors; the bottom teams were the home team.
Data Analysis
Miles and Huberman (1994) stated that,” We all have our preconceptions,
our pet theories about what is happening. The risk is taking them for granted,
imposing these willy nilly, missing the inductive grounding that is needed.” Miles
and Huberman also noted that these principles are naturally abstract and

16
discussed five main methods and four supplementary methods with which to
further clarify the descriptions. They stated that in effects-displays, or examining
diverse results (win, lose, or draw) occurring from a single major variable
(defensive turnovers) are structured as:
Table 4:
Probable Cause/Event
Probable Cause/Event
Effect 1
Effect 2
Effect 3
Effect 4
They further stated that when there are several cases where an important or
salient “cause” (in this case, turnovers in the defensive half) is expected to have
a variety of results (win, lose or draw), the question is how to display relevant
data, to see how the effects play out across an array of cases that have a greater
or smaller amount of the basic cause (turnovers in the defensive half).
Research questions
In this study, the researcher addressed the following questions:
1.) What are the contributing factors to game wins and losses across several
sports?
2.) What research has been done regarding, wins, losses, and draws in
soccer?

17
3.) What impact do turnovers in a team’s defensive half, shots, and shots on
goal have on wins, losses, and draws for college level soccer teams?
Definition of terms
For the purposes of this study, the researcher defined the following terms as:
50-50 Ball:
A ball that arrives with a 50% chance of retrieval from either team
Shot:
When a player makes an attempt to score by striking a ball in the direction of the
goal where the ball would or would not have actually scored, hit the goal’s frame,
or necessitated a save by the defensive team or its goal keeper.
Shots on Goal:
When a player makes an attempt to score by striking the ball in the direction of
the goal where the ball would score, or hit one of the posts, unless otherwise
saved by the defensive team or its goal keeper.
Turnover in the Defensive Half:
a.) A ball being passed from a player on the defensive half of the field
being intercepted by an opponent within a twenty yard radius to the
intended receiver on the attacking half of the field.
b.) A ball passed to an intended receiver already on the defensive half of
the field at any distance that is intercepted by an opponent.
c.) A player losing the ball in the defensive half to an opponent while

18
d.) A player improperly clearing the ball from their defensive half, in that
the ball was either completely missed or miss-hit so that the ball stayed
in the general area in which it was being cleared from.
e.) A goal keeper mishandling a catchable shot or cross and being
Limitations of the Study
Limitations of this study include the number of games analyzed, the
number of different teams analyzed, and skill level (based on NCAA division)
used in the study. Due to time constraints, the researcher limited the number of
games analyzed to ten games and fourteen different teams. This contradicts
some of the previous research in asking researchers to use more data and
clearly, having a wider base for games, teams, or even leagues, would give a
fuller understanding of the statistics provided, as is the case with most data
collection.
Delimitations of the Study
Several delimitations were noted for this study. The research was
delimited to fourteen different NCAA teams; Bowling Green State University,
Indiana University, Marquette University, Michigan State University,
Northwestern University, Notre Dame, Ohio State University, Pennsylvania State
University, University of Akron, University of Louisville, University of Michigan,
University of Wisconsin-Green Bay, University of Wisconsin-Madison, and
University of Wisconsin-Milwaukee. To ensure the researcher did not inflate the
statistics by choosing particular games that may skew the statistics to prove the

19
hypothesis, the researcher watched games of teams that were broadcast on one
of two local networks: The Big Ten Network and WISN Milwaukee. At no point
did the researcher view the previously charted game statistics of the matches
that were viewed. All statistics were taken first hand by the researcher.
The number of teams analyzed by the researcher was limited to fourteen.
Skill level was limited to NCAA division I soccer teams, specifically; Bowling
Green State University, Indiana University, Marquette University, Michigan State
University, Northwestern University, Notre Dame, The Ohio State University,
Pennsylvania State University, University of Akron, University of Louisville,
University of Michigan, University of Wisconsin-Green Bay, University of
Wisconsin-Madison, and University of Wisconsin-Milwaukee. Having limited the
research to ten games and fourteen teams necessitates caution in generalizing
to a larger population.
Overview of chapters
Chapter Two of this thesis is a literature review. First, it examines the
contributing factors to game wins and losses across several sports. Professional
basketball, collegiate basketball, professional football, and collegiate football will
be specifically addressed. Next, the researcher will look at research that has
been done regarding wins, losses, and draws in soccer across various skill
levels. Finally, the researcher will specifically examine the impact of turnovers in
a team’s defensive half, shots, and shots on goal with regard to wins, losses, and
draws for college level soccer teams; this being the researcher's main purpose in
the study.

20
Chapter Three focuses on the methods used in this qualitative, collective
case study. It details the process of how the study took place. Included is a
detailed description of the participating teams. Chapter Three also provides a
detailed explanation of game analysis and how it was conducted and analyzed.
Chapter Four reports and interprets the findings of this qualitative study.
Data collected from fourteen teams and ten games are included.
Chapter Five summarizes the implications for the results and findings of
the qualitative study. Chapter Five also discusses recommendations for further
research.

21
Chapter Two: Literature Review
Introduction
coaches, and players alike. Numerous studies have been conducted which
cover, in detail, tangible statistics such as: shots on goal, shots, corner kicks,
goal kicks, free kicks, and in some cases, consecutive passes. In looking at
research conducted for other sports such as football and basketball, the effects
of turnovers on wins or losses have been studied and discussed in depth. The
researcher saw a gap in current and past soccer research regarding a lack in
turnover statistics. The effect of turnovers, and more specifically, the effect of
turnovers in the team’s defensive half in soccer, however, has been limited. It
was the researcher’s goal in this study to help close this gap.
Soccer statistics are taken and presented with no subjectivity, some
subjectivity and a lot of subjectivity. Some statistics will be a part of nearly every
game taking place on the planet. No matter how big or how small, goals will be
noted and by the end of the game, this will be the telling statistic as to which
team wins the game and in most cases, which team is better. In most
competitive matches, shots, shots on goal (that is a shot that is either saved,
scored, or strikes the goal’s post or crossbar), corner kicks, fouls and ejections
will be kept. If these statistics are not kept with a pen and paper they are
generally recalled with relative familiarity by on-lookers. Statistics that are kept in

22
some of the worlds’ more contested matches include; time of possession, saves,
fouls surrendered, free kicks taken, offside calls for and against and the
researcher proposes to add turnovers in the defensive half of the field.
American football.
Sports have been analyzed, and obsessed about throughout the world for
what seems like an eternity. Wins and losses are always at the forefront of any
discussion. In looking at American football, turnovers and their effect on the
games’ outcome have been intensely covered and scrutinized. When the
Packers met the Steelers in the 2011 NFL Super Bowl, the stakes could not have
been higher. When the Packers ended up winning the game, the Steelers were
ridiculed more for their lackluster play than for the Packers’ offensive prowess.
Quarterback, Ben Roethlisburger accepted some of the game’s blame in saying,
"They're a great defense. They got after us [in the first half], and I turned the ball
over, and you can't do that" (McClain, 2011, p.1). Roethlisburger’s teammate,
running back, Rashard Mendenhall, continued the sentiment by saying “When
you turn the ball over like we did, you put yourself in a bad position” (Newberry,
2011, p.1). This information suggests that the multitude of turnovers lost the
game for the Steelers more than their ability to score points. This could be a
small sample size. In looking at the past five Super Bowls, however, the
researcher finds:

23
Table 5:
2006-2010 Super Bowls
Super Bowls Fumbles Interception
s
Total
Turnovers
Score
2010
Packers 0 0 0 31
Steelers 2 1 3 25
2009
Saints 0 0 0 31
Colts 0 1 1 17
2008
Steelers 0 1 1 27
Cardinals 1 1 2 23
2007
Giants 0 1 1 17
Patriots 1 0 1 14
2006
Colts 2 1 3 29
Bears 3 2 5 17
Game Totals
Winners 2 3 5 135
Losers 7 5 12 96
Taken from: http//www.espn.com
In the last five Super Bowls, the team that won the game also had fewer
turnovers. This is not to say that it is impossible to win a football game while
committing turnovers, but it does imply a greater difficulty in achieving the sport’s
greatest victory while amassing more turnovers than the competition.
The average margin of victory in these five games was 7.8 points, or, one
touchdown and a two point conversion. Interestingly enough, the average
turnover differential was 1.4 per game, which can be further detrimental when
one considers the fact that turnovers often lead to immediate points by the
defense. In four of the five Super Bowls mentioned, the team that won the game
scored a touchdown on an interception and/or a fumble recovery; these are

24
considered turnovers. Those scores, in the case of the 2010, 2009, and 2008
Super Bowls, were while the attacking team was within 40 yards of scoring points
of their own.
Consider the 2008 Super Bowl. The score was: Steelers 10, Cardinals 7;
the Cardinals were 3 yards from scoring with under a minute to play in the half. If
the Cardinals scored a touchdown, they would take a lead of 14-10 into halftime,
and presumably control of the game. As it happened, the Cardinals threw an
interception that was taken back 100 yards by a Steelers player for a touchdown
with no time remaining; a turnover. The score at halftime was Steelers 17,
Cardinals 7. That was a 14 point swing and proved to be insurmountable for the
Arizona Cardinals. These statistics suggest that not only are turnovers impactful
on the score, but they also usually imply a loss. It could also be argued that when
turnovers lead to a score by the opposing team, it can be a mental backbreaker
for the team’s psyche in how they perceive and continue to play the game.
College football statistics show similar results to professional football with
regard to turnovers. In a 2003 college football game, “Matt Kegel threw three
touchdown passes and steadily guided the No. 21 [Washington State] Cougars,
who took advantage of seven-first half turnovers yesterday to beat the 10th-
ranked Ducks, 55-16” (New York Times, 2003, p. 1). In a 2002 college football
game, “Ronnie Brown ran for two touchdowns and Auburn took advantage of five
turnovers to upset visiting Louisiana State, 31-7. Unranked Auburn intercepted
Marcus Randall four times and held LSU, ranked No. 7 by the New York Times
computer and No. 10 in the Associated Press poll, to 242 yards after giving up 68

25
points in its last two games, both losses” (New York Times, 2002, p. 1). In yet
another instance where turnovers are the headline in college football:
Coming into today's game against Army, the New Mexico State coach,
Tony Samuel, told his players they would have to cut down on turnovers if
they were to end a three-game losing streak. The (New Mexico State)
Aggies had committed four turnovers in losing to Nevada by a touchdown
a week ago, and had fumbled the ball away 10 times in their first six
games, including a 35-7 upset of then 22d-ranked Arizona State on Sept.
8. (Cavanaugh, 1999, p. 1)
In looking at these three games, it is clear that the authors have stressed the
importance of turnovers in each game’s outcome. In another game where the
highly rated West Virginia Mountaineers lost to the University of Connecticut
Huskies, an article by the Associated Press (2010) reported:
Dave Teggart hit a 27-yard field goal in overtime and Connecticut beat
visiting West Virginia, 16-13, giving the Huskies their first win over the
Mountaineers. The winning score was set up when Connecticut linebacker
Lawrence Wilson recovered a fumble inside the 5, the fourth turnover of
the night by West Virginia. (2010, p. 1)
It becomes necessary for the Huskies to win the turnover battle when asked to
beat a seemingly superior team. The Associated Press furthered their point in
discussing turnovers when, “the Mountaineers had 414 yards of offense, but lost

26
four of seven fumbles, and scored just 3 points after the first quarter” (p. 1). The
conclusion may be drawn that without the several turnovers, the Mountaineers
would have had a much greater chance at winning the game.
Professional basketball.
Crossing into other sports, similar trends develop in basketball. The 2010
NBA finals saw two evenly matched teams play seven highly contested matches.
In all but one of the matches (game five), the team with more rebounds and
forced turnovers won the game. In looking at the gross numbers across the
series from the National Basketball Association (www.nba.com), the Lakers saw
the ball seventeen more times than the Celtics did. That is seventeen more
opportunities to score over a series where the difference between a win and a
loss was a mere 3.4 points per game. Could it be possible that instead of
teaching better means of attack and possession, teams should instead be
promoting forced turnovers and high intensity levels on 50-50 balls? A 50-50 ball
is considered a ball that has a fifty percent chance of being won by either team.
Teams that promote more hustle and fundamentals of the game tend to win in
this category. As is stated by Stuart Kantor editor of www.hoopmechanix.com:
Finding players who want to shoot and score is easy; finding players who
want to shut down an opponent's offensive weapon is difficult. Why? Read
the box score. Great defenders aren't fully recognized in the box score the
way offensive players are. Box scores highlight rebounds and blocks, but
rarely publicize how many charges were taken. More importantly, only
close observation of the game can detect truly outstanding defense, for

27
there is no box score category for cutting one’s man off at the baseline.
There is no category for impeding a player who is trying to cut in front of
your face and gain position. (p. 27)
What plagues so many sports is that the better players prefer the more high
profile tasks. The great soccer players tend to play striker, so they can earn
goals, the great football players tend to prefer offense to score touchdowns and
basketball players are similarly praised for their efforts on the offense.
College basketball.
In a playoff game between Manhattan College and University of
Wisconsin-Green Bay, the New York Times (1992) reports, “A desperation
inbounds pass the length of the court by Wisconsin-Green Bay was intercepted
by the Jaspers' Keith Bullock, and Manhattan had its first post-season victory
since 1975” (p. 8). This is one of several cases where a seemingly over-matched
opponent has earned a victory because of multiple turnovers or a poorly timed
turnover. As reported by the New York Amsterdam News’ Jaime C. Harris
(2007), “Hampton University's defensive game plan was evident from the
opening tip-off. Pressure, pressure and more pressure! The Pirates harassed the
Howard University Bison from baseline to baseline and turned 25 Bison turnovers
into easy baskets in coasting to a 65-31 victory” (p. 1). Later in the article Pirates'
head coach Kevin Nick-leberry said, “It was a good win for our program, we didn't
play great offensively, but we played well defensively. I think anytime we play
well defensively, we have a chance to win. And that's been the constant for us”
(p. 1).

28
Kevin Burke and Michelle Burke did a study on the perception of
momentum in both collegiate and high school basketball arena. As they reported
in the Journal of Sports Behavior (2009):
The five most frequently occurring actions at the beginning of perceived
momentum in rank order were a 3-point shot, defensive stop, steal,
fastbreak, or a turnover. During momentum, the five most frequently (in
rank order) occurring actions were turnovers, crowd noise, defensive
stops, steals, and "string" of unanswered points. The five actions most
frequently observed (in rank order) at the end of momentum were
turnovers by momentum team, missed shots by momentum team, time
outs, fouls, and end of the playing period. (2009, p. 303)
Burke and Burke have rated three categories; how momentum starts, during
momentum and how momentum tends to end. In starting momentum, the
second, third and fifth most frequent reasons for momentum were defensive
plays. In sustaining momentum, the first, third and fourth were defensive plays,
and in ending momentum three of the five reasons, including the number one
reason, turnovers, were also defensive plays.
Burke and Burke (2009) initiated a study that was largely based on
offensive performance or momentum. One of the earlier cited definitions of
momentum was provided by Iso-Ahola and Mobily (1980) who stated that
momentum is, “A gained psychological power which may change interpersonal
perceptions and influence physical and mental performance” (p. 1). The
researcher finds it interesting that in an attempt to prove offensive prowess and

29
to determine why teams get momentum in scoring an abundance of points, they
have found in each of their three categories that defensive plays were more
responsible than offensive plays.
Research regarding wins, losses, and draws in soccer
General soccer.
In soccer research, reasons one team won, lost or drew with an opponent,
tend to be:
a.) Shots
b.) Shots on Goal
c.) Possession
d.) Corner Kicks
e.) Fouls (Coinciding with Direct Kicks)
f.) Ejections
In some cases researchers go so far as to count consecutive passes in hopes for
a correlation to winning games. Such is the case with Greevy et al. (2009):
The data show the percentages of single, double, triple, and quadruple
(plus) passes made during the particular half. The data across halves are
largely consistent, except that there is more variability in the first half than
the second. Inspection of Figures 1 and 2 shows that more than 50% of
the passes in the first half were single passes with increases during the
last three games of the reason. The data for the remaining pass
sequences are relatively stable, with little evidence of trends. Two pass
sequences ranged from 25% of passes to about 35%. Three pass

30
sequences consistently ranged between 10 and 20% of all passes. Four
pass sequences were consistently at or below 10% of passes, with a few
exceptions. The outcome of each game is shown as a win (W) or loss (L)
above each data point. Inspection of the relationship between the
proportions of single or multiple passes shows no consistent pattern
associated with wins and losses. (p. 1)
In the Greevy et al. study of nine games over the course of Cortland State
College of New York’s men’s soccer team, showed no patterns of greater
success in consecutive passes in regard to winning games. Had Greevy et al.
had another chance at their research, it may have been important to not only
count the consecutive passes but count the passes that advance the team down
the field. Along those lines, counting passes completed in the attacking half or
attacking third of the field would be the more dangerous and difficult area to
complete passes which could imply more skill by whichever team is able to do
so. With soccer being a game that has so many different playing styles, it is
more than possible that a highly successful team may not complete many passes
in any area of the field but prefer to play a ball long allowing their athleticism to
dictate their success.
Had Greevy et al. (2009) had an opportunity to look at more advanced
players, such as Division I soccer or professional soccer, they may have seen
significance in their study with pass completion. With teams such as the World

31
Cup Champion’s, Spain, tallying nearly 60% possession in the tournaments’ final
match, it is likely that they had more consecutive passes than their competition,
The Netherlands.
Shots taken and shots surrendered are the mainstay of nearly all soccer
matches. It is the one statistic that is almost always given after every match and
nearly all competitive teams across the world keep the statistic in competitive
games. In looking at the 2010 World Cup’s final eight games, the team that
earned more shots won their match 50% of the time. With shots dictating the
winner only 50% over these eight games, the researcher wanted to look at more
statistics regarding these matches. Shots on goal can be statistic that can be
more telling than shots. Shots on goal are defined as a shot taken that scores,
are saved by the goal keeper or strikes the goal post or cross bar. In the final
eight games of the 2010 World Cup, the teams that had more shots on goal than
their opponent won 62.5% of the time. Of the sixteen teams of these eight World
Cup matches, there were six teams that tallied five shots on goal or less, of those
six teams two of them actually won but each of those two teams played a team
that tallied four and two shots on goal respectively.
In the 2010 NCAA final three games, between divisions I, II, and III, the
team with more shots won 77% of the time and the team with more shots on goal
won 56% of the time, which is shown with Table 6:

32
Table 6:
2010 NCAA Tournament
Division I
Match-up
Shots Shots On Goal Score
Akron
Louisville
19
15
7
7
1
0
Louisville
North Carolina
11
9
4
3
2
1
Akron
Michigan
22
9
8
1
2
1
Division II
Match-up
Northern Kentucky
Rollins College
11
14
9
11
3
2
Northern Kentucky
Dowling College
11
5
5
2
4
1
Rollins College
Midwestern State
13
10
5
8
2
1
Division III
Match-up
Messiah College
Lynchburg College
12
10
2
4
2
1
Messiah College
UW-Oshkosh
18
10
8
5
4
1
Lynchburg College
Bowdoin College
14
18
9
5
2
1
Data taken from: http://www.ncaa.com
It is clear that shooting in a soccer match is an important part of the game,
in this instance, shooting more than the opponent won 77% of the time. A team
that takes no shots has no chance of winning. This is not to imply that merely
shooting at random will guarantee success. Shots and shots on goal are
wonderful statistics to look at in getting a general feel for how a team did during a

33
particular match, however, the researcher still thinks there is a gap in the data
and believes more statistics should be used in measuring a team’s successes or
failures.
Time of possession is a loosely defined statistic that is kept at nearly every
professional soccer match. Pollard and Reep (2007) define possession as
follows:
committed). (p. 1)
Although time of possession is formerly followed and expected to show which
team is commanding the field, it does not always correlate into wins, losses and
draws. In the 2010 World Cup the team that led in possession for the final eight
games won 75% of the time.
In looking at time of possession, the reader must understand that it is a
subjective topic. Depending on which news outlet a reader chooses to use, there

34
may be very different records of the exact same game. Table 7 shows four media
outlets reporting the World Cup final between the Netherlands and Spain, all four
outlets show different statistics regarding time of possession.
Table 7:
Possession in the 2010 World Cup Final
Source Spain The Netherlands
soccerstats.com 62.9% 37.1%
FIFA.com 57% 43%
theage.com.au 56% 44%
news.bbc.co.uk 60% 40%
With the commonality of this statistic and its usage in soccer matches
across the world, it is clear that subjective statistics are accepted and used to
show fans, players, and coaches alike the success or failures of given teams.
Similar to time of possession, the statistics of turnovers in the defensive
half would also be subjective in nature. Almost assuredly different statisticians
would keep the number in a slightly different manner. The researcher
understands the discrepancy in who is keeping the statistic but wonders if this is
the main reason for the statistic not being kept when similar statistics, like time of
possession, are kept for nearly all major professional soccer matches?
Corner kicks are a set play that occur when the defensive team is last to
touch the ball over their defensive end line that did not result in a goal. Corner
kicks tend to be a product of an attacking team putting pressure on the defensive
team, where the defensive team is looking to clear the ball, block a shot, or tackle

35
a ball carrier deep in their defensive territory. In the final eight games of the 2010
World Cup the team that won the more corner kicks won the match 62.5% of the
time. This is situation similar to shots and shots on goal where the teams with
more corner kicks tend to win their matches. Although it must be noted that
when a team gets a lead, there is a tendency to play more defensive which
allows the opposition to attack with more vigor and opportunity.
When looking at fouls and ejection, it is important to also think about the
restart of play. When a foul occurs in a soccer match the ball can be restarted in
one of two manners.
a.) Direct free kick: the ball is placed at a standstill, the nearest defenders
keeps a distance of ten yards or greater from the ball. Upon striking the
ball, a goal may be scored without any other players touching the ball.
b.) Indirect free kick: the ball is placed at a standstill, the nearest defender
keeps a distance of ten yards or greater from the ball. Upon striking the
ball, a goal may only be scored when the ball is touched by a second
player from either the attacking or defensive team.
Direct free kicks are far more common and are issued when a player trips,
charges, pulls/holds, tackles or by other means impedes an opposing player.
Direct free kicks have been charted for effectiveness but Allison Alcock from the
Australian Institute of Sport has found is that the sheer number of direct kicks is
not nearly as important was where the direct kicks are actually taken. Alcock
finds:

36
The potential for a direct free kick to result in a goal is largely dependent
on the pitch location from where it is taken, as this influences the distance
the player must kick the ball, the positioning of any defensive wall of
players, and the angle to the goal. (p. 1)
Alcock furthers her findings by illustrating the goals scored from direct free kicks
in the women’s World Cup as follows:
Table 8:
1991-2009 Women’s World Cup Statistics
Data taken from: http//ncaa.com
By Alcock’s own admission she was selecting direct free kicks that were taken in
the teams attacking half, which leaves a large amount of fouls and direct kicks
that are taken without any reasonable chance of scoring. Fouls and free kicks
are another interesting side not to a soccer match but rarely do they dictate a
victor, unless the foul turns into an ejection.
In the 109th
minute of the 2006 Men’s World Cup final, Zinedine Zidane
head butted defender Marco Materazzi in the chest drawing a red card and
immediate ejection (Longman, New York Times, p. 1). What is crucial to realize
in a soccer match once a red card (ejection) has been issued, the player
Women’s World
Cup Tournament
Year
Number of
Games
Number of
Goals Scored
Number of
Goals Scored
Number of goals
direct from a free
kick as a
percentage of all
goals
1991 26 99 1 1.01%
1995 26 99 Not Available Not Available
1999 32 123 5 4.07%
2004 32 107 5 4.67%
2009 32 111 7 6.31%

37
receiving the ejection may not be replaced on the field, leaving his team to ten
players while the other team continues with eleven. Although France did not lose
during regulation to Italy, playing ten versus eleven for the remainder of the
match all but assured France would not be able to score during the last minutes
of overtime.
Carl Bialik of the Wall Street Journal writes about a quarterfinal match
between the Netherlands and Brazil in which Brazil was favored to win but came
up short because of an ejection to one of their players:
In the second half of Brazil’s quarterfinal match against the Netherlands,
Felipe Melo made two catastrophic errors that burned the Brazilians. First,
he deflected a Wesley Sneijder shot into the goal when trying to clear it
with his head. Then, with Brazil trailing 2-1, Melo was sent off for doffing
his spikes into Arjen Robben in the 73rd minute. Brazil was forced to play
down a man for the last 20 minutes, and couldn’t come back, exiting in the
quarterfinal stage for the second consecutive World Cup. (2010, p. 1)
In the same 2010 World Cup involving the same team who had previously
benefited from a Brazilian player’s ejection, were victims of their own ejection in
the tournament finale against Spain. Fletcher states (2010), “After gradually
taking a grip on a tense and bad-tempered contest that produced 14 yellow cards
with (Netherlands’) Johnny Heitinga sent off on 109 minutes after picking up a
second yellow card” (p. 1). Unlike the Netherlands previous match with Brazil,
Spain did end up scoring in the extra period with the Netherlands being down a
field player for the final eleven minutes.

38
A team does not merely lose because they have had a certain number of
fouls or a certain number of fouls in a general area that may prompt a goal. Nor
is it fair to say that just because a team has had a player ejected from the match
that their team has no chance at winning. What this does say is that when
looking at these statistics in an all encompassing view, it is important for them to
tell a story. In looking at the 2010 World Cup final, having issued 14 yellow
cards, it was highly likely that one or more players would be ejected from the
match. This is because after the same player is issued two yellow cards, it turns
into a red card, which is an ejection. If one chooses to look at the match
statistics, it becomes very telling as to why Spain won the game.
Table 9:
2010 World Cup Statistics
Spain The Netherlands
Possession 60% 40%
Total Shots 20 11
Shots on Goal 8 5
Corner Kicks 8 6
Fouls 18 28
Ejections 0 1
Data taken from:
http://news.bbc.co.uk/sport2/hi/football/world_cup_2010/matches/match_64
Spain won in every major category. Although the score was only 1-0, the
statistics paint a picture of a dominant victory for the Spanish. The researcher
suggests another statistics be added, turnover in the team’s defensive half.
Conclusion
It is clear that in looking at soccer statistics some of the stats are
indisputable to a statistician, like goals scored, offside calls, corner kicks, foul
calls or ejections, while others are extremely subjective and leave the

39
interpretation up to the stat keeper. Time of possession, shots, and even saves
can look very different depending on who is keeping the statistics. The
researcher contends that adding another statistic with relative subjectivity would
not only improve the significance of game statistics, it would add another point of
merit in how one looks at the makeup of a game.
Statistics of a match may never prove more noteworthy than actually
viewing the match, but it is the researcher’s contention that the gap can be
bridged between statistical analysis and actual viewership of a soccer match.

40
Chapter Three: Methodology
Introduction
coaches, and players alike. Numerous studies have been conducted which
cover, in detail, tangible statistics such as: shots on goal, shots, corner kicks,
goal kicks, free kicks, and in some cases, consecutive passes. In looking at
research conducted for other sports such as football and basketball, the effects
of turnovers on wins or losses have been studied and discussed in depth. The
researcher saw a gap in current and past soccer research regarding a lack in
turnover statistics. The effect of turnovers, and more specifically, the effect of
turnovers in the team’s defensive half in soccer has been limited. It was the
researcher’s goal in this study to help close this gap.
The purpose of this study was to fill the gap in the statistical outcome of
soccer matches by keeping the statistics of turnovers in the defensive half.
Currently soccer has statistics that cover most offensive aspects of the game, but
in a sport where possession, shots, and saves can have multiple definitions,
keeping the statistic of turnovers in the defensive half could also be defined in
many ways. The researcher’s goal was to provide data and conclusions on how
to develop teams in terms of player positional placement, attacking styles and
defensive formations. It was hypothesized that the more information a coach or
team has, the more likely teams will gain an advantage on the competition and
be more successful over the course of a season, or a multitude of seasons.
The definition of possession for the purpose of this study will be taken

41
from Pollard and Reep (2007). In which they state:
committed). (p. 1)
Other definable terms that were used for the researcher’s data collection were:
a.) shot: When a player makes an attempt to score by striking a ball in the
direction of the goal where the ball would or would not have actually
scored, hit the goal’s frame, or necessitated a save by the defensive team
or its goal keeper.
b.) shots on goal: When a player makes an attempt to score by striking the
ball in the direction of the goal where the ball would score, or hit one of the
posts, unless otherwise saved by the defensive team or its goal keeper.
c.) turnovers in the defensive half: A ball being passed from a player on the
defensive half of the field being intercepted by an opponent on the
defensive half of the field.

42
i.) A ball passed to an intended receiver already on the defensive half
of the field at any distance that is intercepted by an opponent.
ii.) A player losing the ball in the defensive half to an opponent while
iii.)A player improperly clearing the ball from their defensive half, in
that the ball was either completely missed or miss-hit so that the
ball stayed in the general area in which it was being cleared from.
iv.)A player turns the ball over to an attacker, and instead of allowing
the attacker to continue towards goal or taking a shot, the defender
fouls the attacker. If he ensuing direct kick scores, it will be
counted as a goal directly off of a turnover in the defensive half.
v.) A goal keeper mishandling a catchable shot or cross and being
In this chapter the researcher will describe a) research design, b)
participants, c) data collection, and d) data analysis.
Research Design
The researcher’s goal was to gain more informed information on how to
develop teams in terms of player placement, attacking styles and defensive
formations. It was hypothesized that the more information a coach or team has,
the more teams will be allowed to gain an advantage on the competition over the
course of a season or a multitude of seasons.
Is there a direct correlation to more turnovers in a team’s defensive half,
shots surrendered, and shots on goal surrendered, to losing matches? For this

43
study, the researcher narrowed the research down to turnovers in the defensive
half because those are the plays that tend to result in shots for the opposing
team.
The researcher looked at ten different NCAA men’s soccer games; the
games were chosen based on which games were being broadcast by The Big
Ten Network or WISN Milwaukee. The statistics were taken first hand by the
researcher; all games took place throughout the fall 2011 soccer season. This
study followed a qualitative design, using collective case study methods. Data
were obtained and collected using the statistics drawn from ten games across
fourteen NCAA Division I men’s soccer teams.
The researcher viewed ten games for fourteen different NCAA teams;
Michigan State University, Northwestern University, Notre Dame, The Ohio State
of Wisconsin-Madison, and University of Wisconsin-Milwaukee. To ensure the
researcher did not inflate the statistics by choosing particular games that may
skew the statistics to prove the hypothesis, the researcher watched game tapes
of teams that were broadcast on one of two local networks: The Big Ten Network
and WISN Milwaukee. At no point did the researcher view the previously charted
game statistics of the matches that were viewed. All statistics were taken first
hand by the researcher.
The research design for this study was qualitative. A collective case study

44
was done. Johnson and Christensen (2008) define case study research as
“research that provides a detailed account and analysis of one or more cases” (p.
406). Further, Creswell (2008) defines collective case studies as “case studies in
which multiple cases are described and compared to provide insight into an
issue” (p. 439). In the instance of this study, turnovers in the defensive half is the
issue explored through ten separate cases of NCAA Division I soccer matches
throughout the fall 2011 soccer season.
Johnson and Christensen (2008) explain that a collective case study
allows the researcher to compare several cases for similarities and differences
(p. 408). Johnson and Christensen also emphasize that “one can more effectively
test a theory by observing the results of multiple cases” and “one is more likely to
be able to generalize the results for multiple cases then from a single case” (p.
408). Studying multiple cases allowed this researcher to draw conclusions with
greater confidence as a result.
Participants
NCAA Division I Athletics.
The participants for this collective case study were players that were on
teams being broadcast by The Big Ten Network or WISN Milwaukee. These were
simply the Division I soccer games that were on TV during the fall of 2011. All
statistics were taken first hand by the researcher and at no point during the study
did the researcher have any interaction with the teams, players, or coaches.
According to its website, the NCAA oversees 89 championships in 23
sports. There are more than 400,000 student-athletes competing in three

45
divisions at over 1,000 colleges and universities within the NCAA. The National
Collegiate Athletic Association (NCAA) defines the parameters of its three
divisions on its website http://www.ncaa.org as follows:
Colleges and universities determine the level at which they will compete,
and new members must petition to join the division they choose. Once
division affiliation is determined, members must comply with rules
(personnel, amateurism, recruiting, eligibility, benefits, financial aid, and
playing and practice seasons) that vary from division to division.
The division structure enables each NCAA member institution to choose
the level of competition that best fits its mission. The NCAA does not
assign membership classification. NCAA rules permit limited multidivision
classification.
a.) Division II programs may classify one men’s and one women’s
sport at the Division I level.
b.) Division III programs may sponsor one men’s and one women’s
program at the Division I level but cannot offer athletically related
financial aid in those sports (several Division III members were
grandfathered in under previous rules and are permitted to provide
aid in those sports).
c.) Division I members may not classify any of their sports in other
divisions. (http://www.ncaa.org)

46
The researcher chose cases that only involved NCAA Division I schools. The
NCAA further defines the requirements of Division I athletic programs as follows:
Division I members must offer at least 14 sports (at least seven for men
and seven for women, or six for men and eight for women). The institution
must sponsor at least two team sports (for example, football, basketball or
volleyball) for each gender. The school also must have participating male
and female teams or participants in the fall, winter and spring seasons.
Each Division I program must play a minimum number of contests against
Division I opponents. The minimums vary by sport. (http://www.ncaa.org)
Schools.
Bowling Green State University.
According to http://www.bgsu.edu, Bowling Green State University is a
Division I soccer program. The men’s soccer team was led by head coach Eric
Nichols in 2011. Bowling Green was founded in 1910, in Bowling Green, Ohio.
As of 2001, they had nearly 20,000 students enrolled in the University.
Indiana University.
According to http://www.indiana.edu, Indiana University was founded in
1820 and is located in Bloomington, Indiana. In 2011, Indiana University’s
enrollment was nearly 41,000 students. The head coach of the Indiana Hoosiers
men’s soccer team in 2011 was Todd Yeagley.
Marquette University.
Marquette University was founded in 1881 in Milwaukee, Wisconsin
according to http://www.marquette.edu. In 2011, Marquette had a student

47
population of nearly 8,200 students. The head coach of the Marquette Golden
Eagles men’s soccer team in 2011 was Louis Bennett.
Michigan State University.
According to http://www.msu.edu, Michigan State University was founded
in 1855, with nearly 48,000 students as of 2011, located in East Lansing,
Michigan. The head coach of the Michigan State Spartans men’s soccer team in
2011 was Damon Rensing.
Northwestern University.
Northwestern University was founded in 1851 in Evanston, Illinois. It 2011,
it had a population of nearly 8,100 students. According to
http://www.northwestern.edu, the head coach of the Northwestern men’s soccer
team was Tim Lenhahan in 2011.
University of Notre Dame.
According to http://www.nd.edu, the University of Notre Dame is located in
South Bend, Indiana, with nearly 12,000 students as of 2011. It was founded in
1842. The head coach of Notre Dame men’s soccer in 2011 was Bobby Clark.
The Ohio State University.
The Ohio State University Buckeyes are located in Columbus, Ohio.
According to http://www.osu.edu, the university was founded in 1870 and in
2011, had a student population of nearly 57,000. The head coach of the
Buckeyes men’s soccer team in 2011 was John Bluem.

48
Pennsylvania State University.
The Pennsylvania State University website, http://www.psu.edu, states
that the university was founded in 1855 and is located in University Park,
Pennsylvania. The university had nearly 39,000 students in 2011. The head
coach of the Nittany Lions men’s soccer team in 2011 was Bob Warming.
University of Akron.
According to http://www.uakron.edu, the University of Akron is locaed in
Akron, Ohio, had a population of nearly 26,000 students in 2011, and was
founded in 1870. The head coach of Akron men’s soccer in 2011 was Caleb
Porter.
The University of Louisville.
The University of Louisville is located in Louisville, Kentucky. It was
founded in 1798, with a student population of nearly 22,000 in 2008. Louisville’s
head men’s soccer coach in 2011 was Ken Lolla. This is according to
http://www.louisville.edu.
University of Michigan.
According to http://www.umich.edu, the University of Michigan is located in
Ann Arbor, Michigan, with a student population of nearly 42,000 students in
2011. It was founded in 1817. The head coach of the men’s soccer team in 2011
was Steve Burns.
University of Wisconsin-Green Bay.
The University of Wisconsin-Green Bay is a Division I school, located in
Green Bay, Wisconsin and founded in 1965. In 2011, it had a student population

49
of nearly 6,600 students according to http://www.uwgb.edu. Green Bay’s head
coach in 2011 was Kris Kelderman.
The University of Wisconsin-Madison.
The University of Wisconsin-Madison was founded in 1848 and had a
student population of nearly 43,000 students in 2011. It is located in Madison,
Wisconsin, according to http://www.wisc.edu. The head coach of the Division I
men’s soccer program in 2011 was John Trask.
University of Wisconsin-Milwaukee.
The University of Wisconsin-Milwaukee was founded in 1885 in
Milwaukee, Wisconsin. It had a student population of nearly 31,000 students in
2011. According to http://www.uwm.edu, the head coach of the Milwaukee
Panthers men’s soccer program was Chris Whalley in 2011.
Cases
The research design for this study incorporated qualitative events.
Creswell (2008) defines qualitative research as an in-depth exploration of the
“event” of a bounded system which means it is separated out for research in
terms of time, place or some physical boundaries (p. 465). This researcher
conducted a collective case study, where ten NCAA Division I men’s soccer
games were analyzed in order to determine whether or not turnovers in the
defensive half contributed to wins, losses, or draws. The researcher looked at
one event, turnovers in the defensive half, as it related to wins, draws, and
losses, across ten cases. Table 10 illustrates those cases.

50
Table 10:
Events Compared to Cases
Event Cases
Madison vs. University of Wisconsin-
Green Bay
Case 2: University of Wisconsin-Green
Bay vs. University of Wisconsin-
Milwaukee
Madison vs. University of Michigan
Case 4: Bowling Green University vs.
Pennsylvania State University
Case 7: Pennsylvania State University
vs. Michigan State University
Case 8: University of Notre Dame vs.
Marquette University
Case 9: Indiana University vs.
Case 10: University of Akron vs.
Data Collection
The research design for this study was qualitative. A collective case study
was done. Johnson and Christensen (2008) define case study research as
“research that provides a detailed account and analysis of one or more cases” (p.
406). Further, Creswell (2008) defines collective case studies as “case studies in
which multiple cases are described and compared to provide insight into an
issue” (p. 439). In the instance of this study, turnovers in the defensive half is the
issue explored through ten separate cases of NCAA Division I soccer matches

51
The researcher viewed ten game tapes for fourteen different NCAA teams;
Michigan State University, Northwestern University, Notre Dame, Ohio State
skew the statistics to prove the hypothesis, the researcher watched games of
teams that were broadcast on one of two local networks: The Big Ten Network
For each game the researcher gathered information for each team,
specifically: turnovers in the defensive half, shots, shots on goal, goals, goals
directly off of a defensive turnover, and game result as a win, draw, or loss. This
information was gathered to analyze the impact that turnovers in the defensive
half would have on a Division I soccer match. The researcher’s main focus was
on turnovers in the defensive half as it pertained to wins, losses, and draws. The
researcher gathered data on shots and shots on goal as well, in order to
compare previously acceptable statistics to show subjectivity.
The researcher gathered this data on a simple spread sheet while viewing
each recorded game and was able to view the game more than once for
accuracy.

52
Data analysis
In the instance of this study, turnovers in the defensive half was the issue
explored through ten separate cases of NCAA Division I soccer matches
The researcher viewed ten game tapes from fourteen different NCAA
teams; Bowling Green State University, Indiana University, Marquette University,
Michigan State University, Northwestern University, Notre Dame, Ohio State
skew the statistics to prove the hypothesis, the researcher watched games of
teams that were broadcast on one of two local networks: The Big Ten Network
Data were then analyzed through an in depth discussion of each case as it
related to the event of turnovers in the defensive half with regard to wins, losses,
and draws.

53
Chapter Four: Findings
Introduction
The overall purpose of this study was to research a variable that has not
been discussed before (turnovers in the defensive half of the field) and evaluate
how these turnovers contribute to wins, losses, and draws in soccer matches.
In a game with so few statistics available to be followed, it was this
researcher’s opinion that soccer needed to collect more statistics, including the
more difficult (qualitative) statistics, such as turnovers in the defensive half.
Furthermore, if the consequences of turnovers in a team’s defensive half
becomes qualitatively significant, it may result in teams re-thinking their offensive
and defensive strategies relative to building their attacks further from their
defensive goal than was previously accepted.
The researcher’s goal was to gain more informed information on how to
develop teams in terms of player placement, attacking styles and defensive
formations. It was hypothesized that the more information a coach or team has,
the more teams will be allowed to gain an advantage on the competition over the
course of a season or a multitude of seasons.
For this study, the researcher narrowed the research down to turnovers in
the defensive half because those are the plays that tend to result in shots for the
opposing team. The researcher looked at ten different NCAA men’s soccer
games; the games were chosen based on which games were being broadcast by
The Big Ten Network or WISN Milwaukee. The statistics were taken first hand
by the researcher; all games took place throughout the fall 2011 soccer season.

54
This study followed a qualitative design, using collective case study
methods. Data were obtained and collected using the statistics drawn from ten
games across fourteen NCAA Division I men’s soccer teams.
In this research, the researcher viewed ten games from fourteen different
Division I NCAA teams; Bowling Green State University, Indiana University,
Marquette University, Michigan State University, Northwestern University, Notre
Dame, The Ohio State University, Pennsylvania State University, University of
Akron, University of Louisville, University of Michigan, University of Wisconsin-
Green Bay, University of Wisconsin-Madison, and University of Wisconsin-
Milwaukee. This was an original study and all research was taken first hand by
the researcher.
The average amount of turnovers in the defensive half was proven to be
6.9 over these ten games. When a team turned the ball over more than 6.9
times, they lost seven times and won five. When a team turned the ball over in
the defensive half less than 6.9 times, they won five and lost three. This
correlation does not mean causation, but does suggest a link.
Data findings
Case 1: University of Wisconsin-Madison vs. University of
Wisconsin-Green Bay.
On October 19th
, 2011, the University of Wisconsin-Madison played a
Division I contest at the University of Wisconsin-Green Bay. Information provided
by http://www.ncaa.com detailed that, UW Madison registered seven shots with
two shots on goal, while UW-Green Bay registered eleven shots, with six shots

55
on goal. In contemporary thinking, UW-Green Bay had the advantage in winning
the game by outshooting their opponent in that shots and shots on goal tend to
dictate the victor. Table 11 shows that not only were UW-Madison outshot; they
also turned the ball over with more frequency in the defensive half. In
observations of the games that followed, the researcher found that when a team
lost by multiple goals, they tended to lose in each of the three statistically kept
categories (turnovers in the defensive half, shots, and shots on goal).
Table 11:
Madison Vs Green Bay
Turnovers
in
Defensive
Half
Shots
Shots
on
Goal
Goals
Goals
directly
off of a
defensive
Turnover
Win/Loss/Draw
University
of
Wisconsin-
Madison
10 7 2 0 0 Loss
University
of
Wisconsin-
Green Bay
7 11 6 2 0 Win
Case 2: University of Wisconsin-Green Bay vs. University of
Wisconsin-Milwaukee.
On September 28th
, 2011, UW-Green Bay traveled to UW-Milwaukee for
an intrastate match. UW-Milwaukee had more shots and more shots on goal than
their opponent, but also had more turnovers in the defensive half. Despite the
turnovers, UW-Milwaukee went on to victory by being one of three games where

56
a goal resulted directly from a turnover in the defensive half. Similar to the other
recorded matches, the team that scored directly off of a turnover in the defensive
half won the game.
In the researcher’s opinion, scoring directly off of a turnover is one of the
most influential causes in winning. In this match, both teams turned the ball over
more than the average of 6.9 so neither was proven to be particularly cautious in
their defensive half. Olsen and Larsen (1997) found that breakdown attacks
(counterattacks) resulted in more scoring opportunities and goals than longer
attacks (elaborate attacks). Further, univariate and multivariate analyses reveal
that counterattacks were more effective than elaborate attacks when playing
against an imbalanced defense. When UW-Milwaukee scored the goal directly off
of a turnover, UW-Green Bay was not in a position to defend, in that they were
caught off guard by the turnover. With the weight of a score directly off of one of
UW-Green Bay’s turnovers, UW-Milwaukee came away with the victory.
Table 12:
Green Bay Vs Milwaukee
Turnovers
in
Defensive
Half
Shots
Shots
on
Goal
Goals
Goals
directly off
of a
defensive
Turnover
Win/Loss/Draw
University
of
Wisconsin-
Green Bay
8 20 9 2 0 Loss
University
of
Wisconsin
Milwaukee
11 24 10 3 1 Win

57
Case 3: University of Wisconsin-Madison vs. University of Michigan.
On October 9th
, 2011, the University of Wisconsin-Madison traveled to the
University of Michigan for a Big Ten Conference match. This game was a
statistical conundrum. Madison had fewer shots, fewer shots on goal, and more
turnovers in the defensive half but still won the match. In statistics, not all games
end the way the numbers may imply. According to Reep and Benjamin (1968)
teams:
…demonstrated the existence of random chance, meaning that despite an
excess of shots by one team in any single match, the opposing team can
still score more goals and thus win the match. However, they also showed
that, in the long run, the team producing the most shots tends to score
more goals with a goal-to-shot ratio of approximately 1:10. This implies
that the actions and outcomes in soccer matches can be described on the
basis of probability (p. 270).
In soccer, the statistic that will always carry the most weight is goals for and
goals against. In this match, Madison won in this category and therefore
deserved to win the game.

58
Table 13:
Madison Vs Michigan
Turnovers
in
Defensive
Half
Shots
Shots
on
Goal
Goals
Goals
directly off
of a
defensive
Turnover
Win/Loss/Draw
University
of
Wisconsin-
Madison
9 6 5 2 0 Win
University
of
Michigan
5 23 6 1 0 Loss
Case 4: Bowling Green University vs. The Ohio State University.
On October 5th
, 2011, The Ohio State University defeated Bowling Green
University 1-0. In this match, Ohio State turned the ball over in the defensive half
less than the average (6.9), had more shots, more shots on goal, and, in the end,
more goals. If the sample size of one game was used to prove a hypothesis, this
would have been the ideal game. Ohio State proved across the statistics of a
match, and in the final score of the match, that they were the superior team. In
this particular instance, the hypothesis has been proven.
Table 14:
Bowling Green Vs Ohio State
Turnovers
in
Defensive
Half
Shots
Shots
on
Goal
Goals
Goals
directly off
of a
defensive
Turnover
Win/Loss/Draw
Bowling
Green
University
8 19 6 0 0 Loss
The Ohio
State
University
6 24 11 1 0 Win

59
Case 5: Northwestern University vs. The Ohio State University.
On October 9th
, 2011, Northwestern University traveled to The Ohio State
University for a Big Ten Conference matchup. Similar to Ohio State’s match from
four days earlier where they won in the four stated statistics, Northwestern won
all four statistics in this match. Northwestern turned the ball over in the defensive
half six times, less than the average (6.9), and had more shots and more shots
on goal than Ohio State while winning the game. This is another instance where
using a small sample size of one game proved the hypothesis.
Table 15:
Northwestern Vs Ohio State
Turnovers
in
Defensive
Half
Shots
Shots
on
Goal
Goals
Goals
directly off
of a
defensive
Turnover
Win/Loss/Draw
Northwestern
University
6 12 6 3 0 Win
The Ohio
State
University
7 11 5 2 0 Loss
Case 6: Northwestern University vs. Pennsylvania State University.
On October 16th
, 2011, Northwestern University visited Pennsylvania
State University in another Big Ten Conference match. In this match,
Northwestern lost in all three categories yet still won the game. When compiling
the statistics for this match, the researcher noted how over matched
Pennsylvania State was. Pennsylvania State had a game plan of clearing the ball
from their defense so that Northwestern’s superior attack would not force
turnovers. As mentioned in the first chapter of this thesis, the talent in Division I

60
soccer matches should be similar from team to team. That was not the case in
this instance. With Northwestern finishing first in the Big Ten Conference, and
Pennsylvania State finishing last, Northwestern appeared to be the better team.
Pennsylvania State was dominated in ball control, 50-50 balls, and overall
physicality by the Northwestern squad.
This was a game where statistics did not tell the full story of the match at
hand. This happens in all sports and is the exact reason why large sample sizes
are encouraged to validate the research. As is stated by Tenga, Ronglan, and
Bahr (2010):
The broader measures of offensive effectiveness, such as scoring
opportunities and shots at goal, are commonly used as an alternative to
goals scored due to the naturally low probability of scoring (about 1%) in
soccer match-play. These measures may enable soccer practitioners to
objectively see behind single match results, which are often influenced by
chance (p. 1).
During research, there are bound to be outliers that simply do not follow the
regular flow of statistics. This game was one of those instances.
Table 16:
Northwestern Vs Penn State
Turnovers
in
Defensive
Half
Shots
Shots
on
Goal
Goals
Goals
directly off
of a
defensive
Turnover
Win/Loss/Draw
Northwestern
University
8 6 4 1 0 Win
Pennsylvania
State
University
3 12 5 0 0 Loss

61
Case 7: Pennsylvania State University vs. Michigan State University.
On October 9th
, 2011, Pennsylvania State visited Michigan State for
another Big Ten Conference game. As Table 17 illustrates, Michigan State had
fewer turnovers, almost three times as many shots, and more shots on goals,
and did not surrender a single shot on goal to Pennsylvania State. This was a
dominating performance by Michigan State, despite the score merely reflecting a
1-0 victory. As mentioned in the other Pennsylvania State match, Pennsylvania
State finished last in the Big Ten Conference. They rarely formulated dangerous
attacks and severely lacked in possession. This was another instance of a
dominating statistical performance for the hypothesis.
Table 17:
Penn State Vs Michigan State
Turnovers
in
Defensive
Half
Shots
Shots
on
Goal
Goals
Goals
directly off
of a
defensive
Turnover
Win/Loss/Draw
Pennsylvania
State
University
6 4 0 0 0 Loss
Michigan
State
University
3 11 7 1 0 Win
Case 8: University of Notre Dame vs. Marquette University.
The University of Notre Dame visited Marquette University on October
12th
, 2011. This game was a perfect microcosm for the hypothesis. With
conventional thinking, Notre Dame won the battle of statistics by outshooting
Marquette, 17-12, and by putting more shots on goal than Marquette at 7-4. Had
the statistics stopped there, it would have looked like Notre Dame was the

62
superior team. But, by digging further, it can plainly be seen that Marquette did a
far better job in controlling the ball on their defensive half (surrendering a mere
four turnovers) and capitalized on one of Notre Dame’s ten turnovers into what
turned out to be the game winning goal. Without keeping the statistics of
turnovers in the defensive half, this game would not have had this avenue of
dissection.
According to http://www.ncaa.com, the 2010 NCAA final three games,
between divisions I, II, and III, the team with more shots won 77% of the time and
the team with more shots on goal won 56% of the time. It is clear that shooting
and shooting accurately can give a team a better chance at winning. This was
true in the game between Marquette and Notre Dame.
Table 18:
Notre Dame Vs Marquette
Turnovers
in
Defensive
Half
Shots
Shots
on
Goal
Goals
Goals
directly off
of a
defensive
Turnover
Win/Loss/Draw
University
of Notre
Dame
10 17 7 0 0 Loss
Marquette
University
4 12 4 1 1 Win
Case 9: Indiana University vs. University of Michigan.
In one of the more dominating performances of the ten games the
researcher watched, Indiana University defeated University of Michigan on
October 15th
, 2011. Indiana turned the ball over in the defensive half only one
time, which is the lowest of the ten games viewed. As was discussed in the

63
previous match, in looking at contemporary statistics, Indiana and Michigan were
even on shots while Michigan held the advantage in shots on goal. When adding
turnovers in the defensive half and goals directly off of turnovers in the defensive
half, Indiana won going away and can be looked at as the clear cut winner.
When watching this game, it was noted by the researcher that Michigan
tended to take more erratic shots, giving them a tally on the stat sheet, but a very
low probability of it turning into a goal. As a result, Michigan was going against a
more formulated and organized defense. A study done by Olsen and Larsen
(1997) showed more scoring opportunities and goals from breakdown attacks
(counterattacks) started when the opponent defense was imbalanced rather than
balanced. This is one of the reasons Indiana University played so well. Since
they rarely turned the ball over in their defensive half, they were rarely playing
defense with an imbalanced formation.
Table 19:
Indiana Vs Michigan
Turnovers
in
Defensive
Half
Shots
Shots
on
Goal
Goals
Goals
directly off
of a
defensive
Turnover
Win/Loss/Draw
Indiana
University
1 13 5 4 2 Win
University
of
Michigan
7 13 7 1 0 Loss
Case 10: University of Akron vs. University of Michigan.
On October 18th
, 2011, the University of Akron visited the University of
Michigan. In this match both teams turned the ball over more than the average of

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