Part one of an analytical study and future report analyzing how historical data can be used better project player performance based on age, position, draft placement and career length.
Part one of an analytical study and future report analyzing how historical data can be used better project player performance based on age, position, draft placement and career length.
Token Authentication in ASP.NET Core--Stormpath WebinarRemy Champion
Stormpath's .NET Developer Evangelist, Nate Barbettini, walks us through token authentication in ASP.NET Core. You will learn how sessions work, how token authentication works, and some of the intricacies of token authentication with ASP.NET Core.
Explore the effect of offensive and defensive team productivity in the NBA on wins, 10+ years of NBA regular season data (2002 – 2013).
Key words: data normalization; directional hypotheses; feauture engineering; ols regression; web scraping
Do lower-seeded teams really play with an "underdog" mentality?Kymee Noll
In my Elementary Statistics class my freshman year of college, our final project was one of our choice. I chose to compare field goal percentages between higher and lower seeded teams in the NCAA Division I Men's Basketball Tournament.
Vanderbilt Football- A New Meaning to Anchor DownCharlie Pallett
Through the lens of athletic administration as well as on-field recruiting, this project aimed explore the interdependent factions of the Vanderbilt University football program and some of the small steps they can begin make to further improve their student athletes’ experience and compete in the SEC.
Please don't hesitate to reach out to charlie.pallettfb@gmail.com if you have any questions!
The Problem of the Chinese Basketball Association Competing for the ChampionshipDr. Amarjeet Singh
In China, basketball is a very popular sport and is loved by the people. The Chinese Basketball Association is China's highest-level basketball league. While watching the game, more and more people want to predict the outcome of the game. There are currently 14 teams participating in the game. According to the rules of the match, the game is divided into two stages: regular season and playoffs. Each game must have a victory, and each team has a fixed number. This article is based on about 100 historical score data from each team for four years, By building a mathematical model, Analyze and calculate the winning probability of the 14 teams. And give a qualitative analysis of the level of the 14 teams in the CBA league.
278 Volume 42 • Number 2 • June 2007Journal of Athletic Tr.docxvickeryr87
278 Volume 42 • Number 2 • June 2007
Journal of Athletic Training 2007;42(2):278–285
� by the National Athletic Trainers’ Association, Inc
www.journalofathletictraining.org
Descriptive Epidemiology of Collegiate Women’s
Soccer Injuries: National Collegiate Athletic
Association Injury Surveillance System,
1988–1989 Through 2002–2003
Randall Dick, MS, FACSM*; Margot Putukian, MD, FACSM†; Julie Agel, MA,
ATC‡; Todd A. Evans, PhD, ATC§; Stephen W. Marshall, PhD��
*National Collegiate Athletic Association, Indianapolis, IN; †Princeton University, Princeton, NJ; ‡University of
Minnesota, Minneapolis, MN; §University of Northern Iowa, Cedar Falls, IA; �University of North Carolina at
Chapel Hill, Chapel Hill, NC
Objective: To review 15 years of National Collegiate Athletic
Association (NCAA) injury surveillance data for women’s soccer
and identify potential areas for injury prevention initiatives.
Background: The number of NCAA schools sponsoring
women’s soccer has grown tremendously, from 271 in 1988–
1989 to 879 schools in 2002–2003. During that time, the NCAA
Injury Surveillance System has collected game and practice in-
jury data for women’s soccer across all 3 NCAA divisions.
Main Results: The rate of injury was more than 3 times high-
er in games than in practices (16.44 versus 5.23 injuries per
1000 athlete-exposures, rate ratio � 3.2, 95% confidence in-
terval � 3.1, 3.4, P � .01), and preseason practices had an
injury rate that was more than 3 times greater than the rate for
in-season practices (9.52 versus 2.91 injuries per 1000 athlete-
exposures, rate ratio � 3.3, 95% confidence interval � 3.1, 3.5,
P � .01). Approximately 70% of all game and practice injuries
affected the lower extremities. Ankle ligament sprains (18.3%),
knee internal derangements (15.9%), concussions (8.6%), and
leg contusions (8.3%) accounted for a substantial portion of
game injuries. Upper leg muscle-tendon strains (21.3%), ankle
ligament sprains (15.3%), knee internal derangements (7.7%),
and pelvis and hip muscle strains (7.6%) represented most of
the practice injuries. Injuries were categorized as attributable to
player contact, ‘‘other contact’’ (eg, contact with the ball,
ground, or other object), or no contact. Player-to-player contact
accounted for more than half of all game injuries (approximately
54%) but less than 20% of all practice injuries. The majority of
practice injuries involved noncontact injury mechanisms. Knee
internal derangements, ankle ligament sprains, and concus-
sions were the leading game injuries that resulted in 10 or more
days of time lost as a result of injury.
Recommendations: Ankle ligament sprains, knee internal
derangements, and concussions are common injuries in wom-
en’s soccer. Research efforts have focused on knee injuries
and concussions in soccer, and further epidemiologic data are
needed to determine if preventive strategies will help to alter
the incidence of these injuries. Furthermore, the specific nature
of the .
Token Authentication in ASP.NET Core--Stormpath WebinarRemy Champion
Stormpath's .NET Developer Evangelist, Nate Barbettini, walks us through token authentication in ASP.NET Core. You will learn how sessions work, how token authentication works, and some of the intricacies of token authentication with ASP.NET Core.
Explore the effect of offensive and defensive team productivity in the NBA on wins, 10+ years of NBA regular season data (2002 – 2013).
Key words: data normalization; directional hypotheses; feauture engineering; ols regression; web scraping
Do lower-seeded teams really play with an "underdog" mentality?Kymee Noll
In my Elementary Statistics class my freshman year of college, our final project was one of our choice. I chose to compare field goal percentages between higher and lower seeded teams in the NCAA Division I Men's Basketball Tournament.
Vanderbilt Football- A New Meaning to Anchor DownCharlie Pallett
Through the lens of athletic administration as well as on-field recruiting, this project aimed explore the interdependent factions of the Vanderbilt University football program and some of the small steps they can begin make to further improve their student athletes’ experience and compete in the SEC.
Please don't hesitate to reach out to charlie.pallettfb@gmail.com if you have any questions!
The Problem of the Chinese Basketball Association Competing for the ChampionshipDr. Amarjeet Singh
In China, basketball is a very popular sport and is loved by the people. The Chinese Basketball Association is China's highest-level basketball league. While watching the game, more and more people want to predict the outcome of the game. There are currently 14 teams participating in the game. According to the rules of the match, the game is divided into two stages: regular season and playoffs. Each game must have a victory, and each team has a fixed number. This article is based on about 100 historical score data from each team for four years, By building a mathematical model, Analyze and calculate the winning probability of the 14 teams. And give a qualitative analysis of the level of the 14 teams in the CBA league.
278 Volume 42 • Number 2 • June 2007Journal of Athletic Tr.docxvickeryr87
278 Volume 42 • Number 2 • June 2007
Journal of Athletic Training 2007;42(2):278–285
� by the National Athletic Trainers’ Association, Inc
www.journalofathletictraining.org
Descriptive Epidemiology of Collegiate Women’s
Soccer Injuries: National Collegiate Athletic
Association Injury Surveillance System,
1988–1989 Through 2002–2003
Randall Dick, MS, FACSM*; Margot Putukian, MD, FACSM†; Julie Agel, MA,
ATC‡; Todd A. Evans, PhD, ATC§; Stephen W. Marshall, PhD��
*National Collegiate Athletic Association, Indianapolis, IN; †Princeton University, Princeton, NJ; ‡University of
Minnesota, Minneapolis, MN; §University of Northern Iowa, Cedar Falls, IA; �University of North Carolina at
Chapel Hill, Chapel Hill, NC
Objective: To review 15 years of National Collegiate Athletic
Association (NCAA) injury surveillance data for women’s soccer
and identify potential areas for injury prevention initiatives.
Background: The number of NCAA schools sponsoring
women’s soccer has grown tremendously, from 271 in 1988–
1989 to 879 schools in 2002–2003. During that time, the NCAA
Injury Surveillance System has collected game and practice in-
jury data for women’s soccer across all 3 NCAA divisions.
Main Results: The rate of injury was more than 3 times high-
er in games than in practices (16.44 versus 5.23 injuries per
1000 athlete-exposures, rate ratio � 3.2, 95% confidence in-
terval � 3.1, 3.4, P � .01), and preseason practices had an
injury rate that was more than 3 times greater than the rate for
in-season practices (9.52 versus 2.91 injuries per 1000 athlete-
exposures, rate ratio � 3.3, 95% confidence interval � 3.1, 3.5,
P � .01). Approximately 70% of all game and practice injuries
affected the lower extremities. Ankle ligament sprains (18.3%),
knee internal derangements (15.9%), concussions (8.6%), and
leg contusions (8.3%) accounted for a substantial portion of
game injuries. Upper leg muscle-tendon strains (21.3%), ankle
ligament sprains (15.3%), knee internal derangements (7.7%),
and pelvis and hip muscle strains (7.6%) represented most of
the practice injuries. Injuries were categorized as attributable to
player contact, ‘‘other contact’’ (eg, contact with the ball,
ground, or other object), or no contact. Player-to-player contact
accounted for more than half of all game injuries (approximately
54%) but less than 20% of all practice injuries. The majority of
practice injuries involved noncontact injury mechanisms. Knee
internal derangements, ankle ligament sprains, and concus-
sions were the leading game injuries that resulted in 10 or more
days of time lost as a result of injury.
Recommendations: Ankle ligament sprains, knee internal
derangements, and concussions are common injuries in wom-
en’s soccer. Research efforts have focused on knee injuries
and concussions in soccer, and further epidemiologic data are
needed to determine if preventive strategies will help to alter
the incidence of these injuries. Furthermore, the specific nature
of the .
Turkey vs Georgia Tickets: Turkey's Road to Glory and Building Momentum for U...Eticketing.co
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of the 2024 Upper Deck NHL Draft could turn out.
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We offer UEFA Euro 2024 Tickets to admirers who can get Denmark vs England Tickets through our trusted online ticketing marketplace. Eticketing. co is the most reliable source for booking Euro Cup Final Tickets. Sign up for the latest Euro Cup Germany Ticket alert.
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Mats André Zuccarello Aasen, commonly known as Mats Zuccarello, was born on September 1, 1987, in
Oslo, Norway. He grew up in the bustling neighborhood of Løren, where his passion for ice hockey began
at a young age. His mother, Anita Zuccarello, is of Italian descent, and his father, Glenn Aasen, is
Norwegian. This multicultural background played a significant role in shaping his identity and versatility
on and off the ice.
Spain vs Croatia Date, venue and match preview ahead of Euro Cup clash as Mod...Eticketing.co
We offer Euro Cup Tickets to admirers who can get Spain vs Croatia Tickets through our trusted online ticketing marketplace. Eticketing.co is the most reliable source for booking Euro Cup Final Tickets. Sign up for the latest Euro Cup Germany Ticket alert.
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Results for LtCol Thomas Jasper, Marine, for the 2010 Marine Corps Marathon held October 31, 2010, marking the 35th annual marathon known as "The People's Marathon."
An impressive finishing time of 3:46:39, placing 324th in the Male division ages 40-44.
Belgium vs Slovakia Belgium announce provisional squad for Euro Cup 2024 Thib...Eticketing.co
Euro 2024 fans worldwide can book Belgium vs Slovakia Tickets from our online platform www.eticketing.co. Fans can book Euro Cup Germany Tickets on our website at discounted prices.
Ukraine Euro Cup 2024 Squad Sergiy Rebrov's Selections and Prospects.docxEuro Cup 2024 Tickets
After securing their spot through the playoff route, Ukraine is gearing up for their fourth consecutive European Championship. Ukraine first qualified as hosts in 2012, but in 2016
Narrated Business Proposal for the Philadelphia Eaglescamrynascott12
Slide 1:
Welcome, and thank you for joining me today. We will explore a strategic proposal to enhance parking and traffic management at Lincoln Financial Field, aiming to improve the overall fan experience and operational efficiency. This comprehensive plan addresses existing challenges and leverages innovative solutions to create a smoother and more enjoyable experience for our fans.
Slide 2:
Picture this: It’s a crisp fall afternoon, driving towards Lincoln Financial Field. The atmosphere is electric—tailgaters grilling, fans in Eagles jerseys creating a sea of green and white. The air buzzes with camaraderie and anticipation. You park, join the throng, and make your way to your seat. The stadium roars as the Eagles take the field, sending chills down your spine. Each play is a thrilling dance of strategy and skill. This is what being an Eagles fan is all about—the joy, the pride, and the shared experience.
Slide 3:
But now, the day is marred by frustration. The excitement wanes as you struggle to find a parking spot. The congestion is overwhelming, and tempers flare. The delays mean you miss the pre-game excitement, the tailgate camaraderie, and even the opening kick-off. After the game, the joy of victory or the shared solace of defeat is overshadowed by the stress of navigating out of the parking lot. The gridlock, honking horns, and endless waiting drain the energy and joy from what should have been an unforgettable experience.
Our proposal aims to eliminate these frustrations, ensuring that from arrival to departure, your experience is extraordinary. Efficient parking and smooth traffic flow are key to maintaining the high spirits and excitement that make game days special.
Slide 4:
The Philadelphia Eagles are not just a premier NFL team; they are an integral part of the community, hosting games, concerts, and various events at Lincoln Financial Field. Our state-of-the-art stadium is designed to provide a world-class experience for every attendee. Whether it's the thrill of game day, the excitement of a live concert, or the camaraderie of community events, we pride ourselves on delivering a fan-first experience and maintaining operational excellence across all our activities. Our commitment to our fans and community is unwavering, and we continuously strive to enhance every aspect of their experience, ensuring they leave with unforgettable memories.
Slide 5:
Recent trends show an increasing demand for efficient event logistics. Our customer feedback has consistently highlighted frustrations with parking and traffic. Surveys indicate that a significant number of fans are dissatisfied with the current parking situation. Comparisons with other venues like Citizens Bank Park and Wells Fargo Center reveal that we lag in terms of parking efficiency and convenience. These insights underscore the urgent need for innovation to meet and exceed fan expectations.
Slide 6:
As we delve into the intricacies of our operations, one glaring issue emer
Narrated Business Proposal for the Philadelphia Eagles
University of Iowa WBB - Team Analysis 2016
1. University of Iowa
Women’s Basketball Program
Team and Player Analysis
Created by: Justin Ullestad
(954)-295-5353
justin.ullestad@gmail.com
www.justinullestad.com
s
2. OVERVIEW
This document will focus on the team’s performance during the 2015-16
season as well as over the past five seasons (2011-2016). In addition, this
document analyzes player performance and progression for athletes
currently listed as part of the University of Iowa Women’s Basketball team
that logged significant minutes during the 2015-16 season.
Using statistical comparisons and advanced metrics, this data can be used to
evaluate the team’s overall performance on the court as compared to other
programs and student athletes currently playing Division I basketball.
The chart below illustrates some of the significant statistics regarding the
performance of the program over the past ten seasons.
Ten-Year Averages as Compared to All Big Ten Teams
W/YR
• IOWA - 10.4 Conference Wins/YR (5th Best)
• Big Ten Average - 9.27 Wins
Win%
• IOWA- 60.8% Conference Win% (5th best)
• Big Ten Average - 50.7%
PPG
• IOWA- 71 PPG (2nd best)
• Big Ten Average - 68.7 PPG
PA/G
• IOWA- 66.47 PA/G (14th Best)
• Big Ten Average - 63.28 PA/G
*All the statistics and information presented in this document were sourced from either the main NCAA
website or Hawkeyesports.com, and then generated through manually entered equations and algorithms.
3. 2015-16 SEASON
Category Actual Rank (of 344) Div. 1 Leader Actual
Scoring Offense 73.2 36 UConn 88.1
Scoring Defense 71.3 302 UConn 48.3
Scoring Margin 1.9 151 UConn 39.7
FG Percentage 45.3 22 UConn 53
FG Percentage Defense 42.1 287 Oregon St. 32.4
FT Percentage 73 65 UConn 80
Rebound Margin 1.1 133 Maryland 15.1
3PT FG Per Game 6.1 117 Sacramento St. 12.5
3PT FG Percentage 32.9 90 Oregon 42.1
Won-Lost Percentage 57.6 136 UConn 100
Assists Per Game 16 24 UConn 21.6
Blocks Per Game 4.7 48 West Virginia 6.5
Steals Per Game 6.1 305 Sacramento St. 14.9
Turnovers Per Game 16 179 Villanova 7.9
Fouls Per Game 15.9 82 UConn 11.1
Assist Turnover Ratio 1 59 UConn 1.82
Turnover Margin -2.52 282 Syracuse 10.08
3Pt FG Defense 33.3 282 Georgia 24.3
3PT FGs Attempted 608 105 Sacramento St. 1318
Fewest Turnovers 527 228 Villanova 252
Fewest Fouls 526 114 Penn 343
Free Throw Attempts 619 67 South Carolina 834
Free Throws Made 452 46 St. Mary's (CA) 559
Turnovers Forced 13.45 305 Sacramento St. 24.16
3PT FGs Made 200 99 Sacramento St. 386
Assists 529 26 UConn 821
Blocked Shots 154 41 UConn 241
Rebounds 1254 101 Baylor 1730
Rebounds Per Game 38 161 GW 48.64
Steals 200 266 Syracuse 477
4. Big Ten Conference Team Rankings
Total Stats (2015-16 season)
PTS: 7th FGM: 7th FGA: 7th FG%: 5th
AST: 1st 3PA: 7th 3PA: 7th 3P%: 9th
TRB: 7th ORB: 8th TOV: 11th FT%: 7th
5. Big Ten Conference Team Rankings
Total Stats Against (2015-16 season)
PTS: 10th FGM: 10th FGA: 10th FG%: 10th
AST: 9th 3PA: 6th 3PA: 6th 3P%: 9th
TRB: 9th ORB: 11th TOV: 12th FTA: 6th
6. Big Ten Conference Team Rankings
Per Game Averages (2015-16 season)
PTS: 7th FGM: 8th 3PA: 5th FTA: 4th
AST: 5th TRB: 6th BLK: 5th STL: 12th
7. Big Ten Conference Team Rankings
Per Game Averages Against (2015-16 season)
PTS: 9th FGA: 12th FGM: 12th FTA: 6th
AST: 9th TRB: 6th 3PA: 4th 3PM: 8th
8. Big Ten Conference Team Rankings
Advanced Stats (2015-16 season)
Poss: 7th Scoring Poss: 7th Scoring Poss%: 6th OE: 7th
eFG%: 4th TS%: 4th TotalS%: 8th ORB%: 6th
15. Returning Players for the 2016-17 Season
Christina Buttenham
Chase Coley
Tania Davis
Ally Disterhoff
Megan Gustafson
Alexa Kastanek
Carly Mohns
16. Christina
Buttenham
Class: Junior
Position: Forward
Height: 6-0
Notes:
2016 Academic All-Big Ten
Valuation Stats:
ORtg = 93.06
Team Rank: 7th
Big Ten Rank: 70
FIC = 82.25
Team Rank: 5th
Big Ten Rank: 64
TS% = 48.25%
Team Rank: 7th
Big Ten Rank: 69
* Rankings include all returning players
w/ 100 minutes played
EOP Visualization for
2015-16 Season
Proportional to the total production
in each stat for the individual
OE
FG%
Pts Ast
18. Chase
Coley
Class: Junior
Position: Forward
Height: 6-3
Notes:
• Most Improved Player award
for team last season
• Recorded six double-doubles
last season
Valuation Stats:
ORtg = 114.70
Team Rank: 2nd
National Rank: 18
FIC = 296.75
Team Rank: 3rd
Big Ten Rank: 19
TS% = 56.4%
Team Rank: 3rd
Big Ten Rank: 25
* Rankings include all returning
players w/ 100 minutes played
OE
FG%
Pts Ast
EOP Visualization for
2015-16 Season
Proportional to the total production
in each stat for the individual
20. Tania
Davis
Class: Sophomore
Position: Guard
Height: 5-4
Notes:
• 2016 Big-Ten All-Freshman
Team
• Led team in assists last season
(Second highest assist total for
a freshman in program history)
Valuation Stats:
ORtg = 100.44
Team Rank: 5th
National Rank: 47
FIC = 193.38
Team Rank: 4th
Big Ten Rank: 39
TS% = 49.77%
Team Rank: 6th
Big Ten Rank: 60
* Rankings include all returning players
w/ 100 minutes played
EOP Visualization for
2015-16 Season
Proportional to the total production
in each stat for the individual
OE
FG%
Pts
Ast
22. Ally
Disterhoft
Class: Senior
Position: Guard
Height: 6-0
Notes:
• 2016 Second Team All-Big Ten
• 2016 Academic All-Big Ten
• 2015 Second Team All-Big Ten
• 2015 Academic All-Big Ten
• 2014 Big Ten All-Freshman
Team
• 2014 Big Ten All-Tournament Team
Valuation Stats:
ORtg = 122.94
Team Rank: 1st
Big Ten Rank: 5th
FIC = 411.00
Team Rank: 1st
Big Ten Rank: 7th
TS% = 63.42%
Team Rank: 1st
Big Ten Rank: 6th
* Rankings include all returning players
w/ 100 minutes played
EOP Visualization for
2015-16 Season
Proportional to the total production
in each stat for the individual
OE
FG%
Pts Ast
24. Megan
Gustafson
Class: Sophomore
Position: Center
Height: 6-3
Notes:
• 2016 All-Big Ten Freshman
• 2016 Big Ten Freshman of the
Week (Feb 22.)
• Led team in FG%, RPG, and
BPG last season
Valuation Stats:
ORtg = 112.41
Team Rank: 3rd
Big Ten Rank: 22
FIC = 311.00
Team Rank: 2nd
Big Ten Rank: 15
TS% = 57.56%
Team Rank: 2nd
Big Ten Rank: 21
* Rankings include all returning players
w/ 100 minutes played
EOP Visualization for
2015-16 Season
Proportional to the total production
in each stat for the individual
OE
FG%
Pts Ast
26. Alexa
Kastanek
Class: Senior
Position: Guard
Height: 5-10
Notes:
• 2016 Academic All-Big Ten
• 2015 Academic All-Big Ten
• Led team in made 3-Point FGs
last season (56)
Valuation Stats:
ORtg = 98.41
Team Rank: 6th
Big Ten Rank: 54
FIC = 133.13
Team Rank: 5th
Big Ten Rank: 44
TS% = 55.56%
Team Rank: 4th
Big Ten Rank: 27
* Rankings include all returning players
w/ 100 minutes played
EOP Visualization for
2015-16 Season
Proportional to the total production
in each stat for the individual
OE
FG%
Pts Ast
28. Carly
Mohns
Class: Junior
Position: Forward
Height: 6-1
Notes:
• Played in nine games during the
2015-16 season before an injury
caused her to miss the remainder
of the season
Valuation Stats:
ORtg = 110.98
Team Rank: 4th
Big Ten Rank: 24
FIC = 34.63
Team Rank: 7th
Big Ten Rank: 80
TS% = 56.40
Team Rank: 5th
Big Ten Rank: 29
* Rankings include all returning players
w/ 100 minutes played
EOP Visualization for
2015-16 Season
Proportional to the total production
in each stat for the individual
OE
FG%
Pts Ast
30. Abbreviations:
+/-: Plus/Minus rating
2P: 2-Point Field Goals
2P%: 2-Point Field Goal Percentage; the formula is 2P / 2PA.
2PA: 2-Point Field Goal Attempts
2PM: 2-Point Field Goals Made
3P: 3-Point Field Goals
3P%: 3-Point Field Goal Percentage.
3PA: 3-Point Field Goal Attempts
3PM: 3-Point Field Goals Made
APG: Assists Per Game
AST: Assists
AST: The percentage of possessions that end in an assist
AST%: Assist percentage is an estimate of the percentage of teammate field goals a
player assisted while he was on the floor.
AST/TO: Assist to Turnover Ratio
BLK: Blocks
BLK%: Block percentage is an estimate of the percentage of opponent two-point field
goal attempts blocked by the player while he was on the floor.
BPG: Blocks Per Game
DRB: Defensive Rebounds
DRB%: Defensive rebound percentage is an estimate of the percentage of available
defensive rebounds a player grabbed while he was on the floor.
DRtg: Defensive Rating; for players and teams it is points allowed per 100 possessions.
DWS: Defensive Win Shares
eDiff: Efficiency Differential
31. eFG%: Effective Field Goal Percentage. This statistic adjusts for the fact that a 3-point
field goal is worth one more point than a 2-point field goal.
EOP: Efficient Offensive Production
FG: Field Goals (includes both 2-point field goals and 3-point field goals)
FG%: Field Goal Percentage
FGA: Field Goal Attempts (includes both 2-point field goal attempts and 3-point field
goal attempts)
FGM: Field Goals Made (includes both 2-point field goal makes and 3-point field goal
makes)
FIC: Floor Impact Counter. FIC is formula to encompass all aspects of the box score into
a single statistic. The intent of the statistic is similar to other efficiency stats, but assists,
shot creation and offensive rebounding are given greater importance.
FIC40: The FIC total presented on a per-40 minute basis.
FT: Free Throws
FT/FGA: Free Throw Factor. It is a measure of both how often a team gets to the line
and how often they make them
FT%: Free Throw Percentage
FTA: Free Throw Attempts
FTA: Free Throws Made
GP: Games Played
GS: Games Started
HOB: Number of buckets a player is directly involved in whether as the scorer or passer
MP: Minutes Played
NetRtg: A pace controlled statistic that measures point differential (+/-) per 100
possessions.
OE: Offensive Efficiency
ORtg: Offensive Rating: for players it is points produced per 100 possessions, while for
teams it is points scored per 100 possessions.
32. Opp: Opponent
ORB: Offensive Rebounds
ORB%: Offensive rebound percentage is an estimate of the percentage of available
offensive rebounds a player grabbed while he was on the floor.
OWS: Offensive Win Shares
Pace: Pace factor is an estimate of the number of possessions per 48 minutes by a team.
PER: Player Efficiency.
Per 36 Minutes: A statistic (e.g., assists) divided by minutes played, multiplied by 36.
Per Game: A statistic (e.g., assists) divided by games.
PF: Personal Fouls
Poss: Possessions; this stat estimates possessions based on both the team's statistics and
their opponent's statistics, then averages them to provide a more stable estimate.
PProd: Points Produced
PPG: Points Per Game
PPS: Points Per Shot
PTS: Points
REB: Rebounds
RPG: Rebounds Per Game
SPG: Steals Per Game
STL: Steals
STL%: Steal Percentage is an estimate of the percentage of opponent possessions that
end with a steal by the player while he was on the floor.
STL/TO: Steals to Turnover Ratio
TotalS%: Total shooting percentage is a measure of shooting efficiency that combines
field goal, 3-point field goal, and free throw percentages.
TOV: Turnovers
TOV%: Turnover percentage is an estimate of turnovers per 100 plays.
33. TRB: Total Rebounds
TRB%: Total rebound percentage is an estimate of the percentage of available rebounds
a player grabbed while he was on the floor.
TS%: True shooting percentage is a measure of shooting efficiency that takes into
account field goals, 3-point field goals, and free throws.
TSA: True Shooting Attempts;
Usg%: Usage percentage is an estimate of the percentage of team plays used by a player
while he was on the floor.
• %FGM: The percentage of a team’s field goals made that a player has while on
the court
• %FGA: The percentage of a team’s field goals attempted that a player has while
on the court
• %3FGM The percentage of a team’s three point field goals made that a player
has while on the court
• %3FGA: The percentage of a team’s three point field goals attempted that a
player has while on the court
• %FTM: The percentage of a team’s free throws made that a player has while on
the court
• %FTA: The percentage of a team’s free throws attempted that a player has while
on the court
• %OREB: The percentage of a team’s offensive rebounds that a player has while
on the court
• %DREB: The percentage of a team’s defensive rebounds that a player has while
on the court
• %REB: The percentage of a team’s total rebounds that a player has while on the
court
• %AST: The percentage of a team’s assists that a player has while on the court
• %TO: The percentage of a team’s turnovers that a player has while on the court
• %STL: The percentage of a team’s steals that a player has while on the court
• %BLK: The percentage of a team’s blocks that a player has while on the court
• %BLKA: The percentage of a teams’ own block field goal attempts that a player
has while on the court
34. • %PF: The percentage of a team’s personal fouls that a player has while on the
court
• %PFD: The percentage of a team’s personal fouls drawn that a player has while
on the court
• %PTS: The percentage of a team’s points that a player has while on the court
W: Wins
Win%: Win Percentage
WS: Win Shares; an estimate of the number of wins contributed by a player.
WS/48: Win Shares Per 48 Minutes; an estimate of the number of wins contributed by
the player per 48 minutes (league average is approximately 0.100)