Statistical Model Report

2015
Seton Hall University –
Sport Management
Patrick Jennings
[NECBL ADVANCED
STATISTICAL REPORT]
A complete statistical analysis of the New England Collegiate Baseball League based on
advanced metrics such as wRC+, OPS+,ERA-, and FIP-

Table of Contents
 Introduction… 1
 OPS as a Run Predictor… 2-5
 Linear Weight Metrics… 6-7
 Comparing Hitters in Different Environments… 8
o (League Averages)
 NECBL Park Factors… 9
 wRC+ and OPS+… 10
 League Findings – Offense… 11-14
 Collegiate Conference Findings – Offense… 15-18
 Performance Improvement Rating – Offense… 19-20
 Predicting Performance – Hitters… 21
o America East… 22-24
o Northeast 10… 25-26
o Patriot League… 27-28
o ACC… 29-30
o Atlantic 10… 31-32
o Atlantic Sun… 33
o Big Ten… 34
o Ivy League… 35
o MAC… 36
o Ohio Valley… 37-38
o Sunshine State… 39
 ERA- and FIP-… 40-41
 League Findings – Pitching… 42-44
 Collegiate Conference Findings – Pitching… 45-48
 Performance Improvement Rating – Pitching… 49-50
 Predicting Performance – Pitchers… 51
o Atlantic Sun… 52
o Big East… 53
o Big Ten… 54
o Ivy League… 55
o MAAC… 56-57
o Pac 12… 58
o Sun Belt… 59
 Miscellaneous Findings… 60-64
 Summary… 65-66
 NECBL Data… 67-110
 References… 111

1 | P a g e
Introduction
This analysis was completed during my summer as a baseball operations intern for the Valley
Blue Sox of the NECBL. The Blue Sox hail from Holyoke, Massachusetts and play in one of
three summer collegiate leagues that are partly funded by Major League Baseball. The league is
comprised of top talent from across the country over various levels including NCAA Division I,
Division II, Division II, NJCAA, and others. There are 12 teams in the league from across the
New England area. They play a 42 game schedule, and so while analyzing statistics over such a
short season teeters on the verge of senselessness, it still provides value when realizing the
context of the data.
The way general managers make decisions on which players to sign in the NECBL is highly, if
not entirely, statistically based. They are not out there scouting the players and giving them the
“eye test” before they offer a contract. They are simply evaluating every piece of information
that is available to them – which is limited in the collegiate world. They listen to scouting
reports from their college coaches and review stats from their spring seasons. While knowing
to look at statistics such as K/BB ratio and OBP over Batting Average and RBI’s is a step in the
right direction, it is still worlds behind what general managers in professional baseball have at
their fingertips. While there’s no way to compute pitch f/x data such as spin rate and release
point, one is able to compute more advanced metrics with the data that is available to us.
This analysis will take you through my process of computing certain advanced metrics among
college seasons and NECBL seasons. These metrics are not only a better indicator of
performance, but also allow a general manager to better predict which conferences perform best
in the NECBL. Keep in mind that I have tried to write this report so that the average baseball
fan with limited knowledge of statistics can understand the concepts and reasons behind these
calculations. While one may be confused at the technical computations of the numbers, my goal
was to explain the underlying reasons as to why these statistics are important and superior.

2 | P a g e
OPS as a Run Predictor
Obviously the ultimate goal of an offense is to score runs - So finding metrics that are more
accurate at predicting runs scored is valuable. OPS (On Base Plus Slugging) is a statistic that is
simply computed by adding a player’s On Base Percentage (OBP) to their Slugging Percentage
(SLG). This is a notable indicator of runs since it combines the two aspects of the game that
directly lead to the ability to score runs – getting on base, and hitting for power.
The graph below shows the relationship between Runs and Batting Average for teams’ seasons
in the NECBL from 2010 through 2015.
The graph below shows the relationship between Runs and OPS for teams’ seasons in the
NECBL from the same period.
It is hard to tell, but the points in the OPS graph are more clustered around the prediction line
than the Batting Average graph.In order to prove this I ran regressions for both sets of data.
50
100
150
200
250
300
350
0.200 0.225 0.250 0.275 0.300 0.325
Runs
Batting Average
Runs vs BA, NECBL 2010-2015
Runs
Prediction Line
50
100
150
200
250
300
350
0.550 0.650 0.750 0.850 0.950
Runs
OPS
Runs vs OPS, NECBL 2010-2015
Runs
Prediction Line

3 | P a g e
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.883043287
R Square 0.7797654
Adjusted R Square 0.776269661
Standard Error 19.84548224
Observations 65
ANOVA
df SS MS F Significance F
Regression 1 87850.12674 87850.12674 223.0586551 2.2573E-22
Residual 63 24812.11941 393.8431652
Total 64 112662.2462
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept -220.8548136 28.0531879 -7.872717155 5.95766E-11 -276.9146358 -164.7949915 -276.9146358 -164.7949915
Batting Average 1641.770431 109.9266239 14.93514831 2.2573E-22 1422.099604 1861.441258 1422.099604 1861.441258
SUMMARY OUTPUT
R Square 0.8180625
Observations 65
ANOVA
Regression 1 92164.75638 92164.75638 283.2727185 5.37317E-25
Residual 63 20497.48978 325.3569806
Total 64 112662.2462
Intercept -187.7761506 22.941657 -8.184942813 1.69485E-11 -233.6213934 -141.9309077 -233.6213934 -141.9309077
OPS 543.4468983 32.28901072 16.83070761 5.37317E-25 478.9224597 607.9713368 478.9224597 607.9713368
Regression for Runs vs BA
Regression for Runs vs OPS
As you can see from the analysis above, the R squared is what determines the accuracy of
prediction. This measure shows how close the data is fit to the regression line – which
essentially shows the percentage of the variable that is explained by the linear model.
The regressions show that OPS is a slightly better predictor of Runs with an R squared of .82
while the R squared for BA is .78.
This is actually much closer than it should be for the game of baseball in general. To make this
point I ran the same test with MLB teams’ statistics for the 2014 season. The results are below.

4 | P a g e
In this case, you can clearly see that the points are tighter around the line in the OPS graph than
the BA graph. This appearance is backed up by the regression analysis.
500
550
600
650
700
750
800
0.220 0.230 0.240 0.250 0.260 0.270 0.280
Runs
BA
Runs vs BA, MLB 2014
Runs
Prediction Line
500
550
600
650
700
750
800
0.600 0.650 0.700 0.750 0.800
Runs
OPS
Runs vs OPS, MLB 2014
Runs
Prediction Line

5 | P a g e
Regression for Runs vs BA (MLB 2014)
Regression for Runs vs OPS (MLB 2014)
As you can see, OPS explains about 82% of runs scored (basically the same percentage as the
NECBL), while Batting average only accounts for about 63% of runs scored.
This proves that OPS is a better run predictor than batting average. The high R squared for
Runs vs BA in the NECBL is surprising, and will probably decrease over time.
SUMMARY OUTPUT
R Square 0.63458142
Observations 30
ANOVA
Regression 1 58207.80567 58207.80567 48.62445619 1.39417E-07
Residual 28 33518.49433 1197.089083
Total 29 91726.3
Intercept -368.1429597 147.3926633 -2.497702067 0.018649023 -670.0631399 -66.22277965 -670.0631399 -66.22277965
AVG 4089.378573 586.4485793 6.973123847 1.39417E-07 2888.093131 5290.664015 2888.093131 5290.664015
SUMMARY OUTPUT
R Square 0.818888903
Observations 30
ANOVA
Regression 1 75113.64914 75113.64914 126.6012387 6.69813E-12
Residual 28 16612.65086 593.3089591
Total 29 91726.3
Intercept -524.0182084 105.2084532 -4.980761454 2.92314E-05 -739.5279525 -308.5084643 -739.5279525 -308.5084643
OPS 1690.241342 150.2206866 11.25172159 6.69813E-12 1382.528219 1997.954465 1382.528219 1997.954465

6 | P a g e
LinearWeightMetrics
The advanced statistical world of baseball outside of MLB is lagging behind. Collegiate teams
are still heavily relying on the basic offensive statistics to evaluate success – batting average,
runs scored, and RBI’s. While these statistics serve a purpose, they do not demonstrate the full
offensive ability of a ballplayer. For example – a player who has 30 singles in 100 at bats is not
as valuable as a player who has 30 doubles in 100 at bats. The doubles equate to more runs for
the team – the ultimate offensive goal. This is where the metrics that involve the Linear
Weights Theory come into play. This theory provides values to each offensive event. These
metrics are Weighted On Base Average or wOBA, Weighted Runs Above Average or wRAA,
and Weighted Runs Created or wRC.
wOBA provides a batting average type statistic. These offensive weights are derived from the
amount of runs that single offensive event produces. For example the following linear weights
were derived from the 2008 MLB season according to the book Beyond Batting Average by Lee
Panas.
1B 2B 3B HR BB HBP SB CS
.47 .77 1.04 1.4 .31 .34 .42 .2
wOBA is calculated as follows
wOBA = (.71*BB)+(.74*HBP)+(.89*1B)+(1.26 *2B)+(1.58*3B)+(2.02*HR)+(.24*SB)-(.51*CS)/PA
PA = Plate Appearances
wOBA is scaled to mimic On Base Percentage (OBP)
Since wOBA is relative to the specific league and year, and depends on the sum of offensive
events, these weights are not universal. Therefore, when calculating wOBA myself, I used a
Markov Theory linear weights calculator developed by famed author Tom Tango. The Markov
theory states that “the probability distribution of the next state depends only on the current
state and not on the sequence of events that preceded it”. This essentially assumes that the idea
of momentum in baseball is nonexistent. This calculator did not have separate entries for BB
and HBP so I combined the two. It also did not have weights for SB or CS and assumed no base
advancement. Therefore I used the .24 weighted value for SB and -.51 weighted value for CS
across the board to account for good/bad base running. Since the way the weights were scaled
to mimic OBP was left unexplained, I used the exact weights the calculator computed, and then
just added .2 to the total wOBA – that seemed to scale it to imitate OBP.

7 | P a g e
Below are the linear weights for the NECBL from 2010-2015
wOBA can then be easily converted to wRAA. This is a run estimator that provides the number
of runs a player contributes to his team above/below what the average player would
contribute. The calculation is as follows.
𝑤𝑅𝐴𝐴 = (
(𝑤𝑂𝐵𝐴 − 𝐿𝑒𝑎𝑔𝑢𝑒 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑤𝑂𝐵𝐴)
1.2
) ∗ 𝑃𝐴
The 1.2 is a wOBA scale. This scales’ calculation was left unexplained in the book Beyond
Batting Average, but it was stated that it is usually approximately 1.2 – so that is what I used
across the board.
The wRC statistic is based on the player’s wRAA and the league average for runs scored per
plate appearance. The calculation is as follows
𝑤𝑅𝐶 = ((
(𝑤𝑂𝐵𝐴 − 𝐿𝑒𝑎𝑔𝑢𝑒 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑤𝑂𝐵𝐴)
1.2
) +
𝐿𝑒𝑎𝑔𝑢𝑒 𝑅𝑢𝑛𝑠
𝐿𝑒𝑎𝑔𝑢𝑒 𝑃𝐴
) ∗ 𝑃𝐴
This measure is a value statistic and has shows how many runs a player is worth to his team.
Year Markov BB/HBP Markov 1B Markov 2B Markov 3B Markov HR Markov K
2010 0.384 0.506 0.811 1.121 1.547 -0.282
2011 0.385 0.504 0.801 1.105 1.532 -0.282
2012 0.427 0.546 0.831 1.113 1.52 -0.358
2013 0.383 0.505 0.812 1.121 1.551 -0.271
2014 0.379 0.499 0.802 1.114 1.541 -0.267
2015 0.382 0.5 0.8 1.109 1.536 -0.275
Total 0.390 0.511 0.811 1.115 1.539 -0.289

8 | P a g e
Year G PA AB H 1B 2B 3B HR TB SB CS K BB HBP SF SH R
2010 252 19289 16744 4192 3227 719 73 173 5576 683 225 3545 1841 350 135 219 2204
2011 252 19040 16570 4182 3122 680 89 291 5913 671 250 3702 1754 404 125 187 2257
2012 206 16165 13998 3904 2694 716 43 451 6059 497 151 3241 1575 337 123 132 2505
2013 287 21734 18840 4727 3678 795 39 215 6245 814 267 4332 2028 430 159 277 2435
2014 252 19303 16730 4119 3158 686 53 222 5577 669 250 3367 1829 380 138 226 2129
2015 251 19326 16789 4194 3222 683 44 245 5700 648 269 3582 1815 371 139 212 2245
Total 1500 114857 99671 25318 19101 4279 341 1597 35070 3982 1412 21769 10842 2272 819 1253 13775
Year R/PA wOBA BABIP AVG OBP SLG OPS ISO K% BB% RC RC/PA
2010 0.1143 0.327 0.305 0.250 0.335 0.333 0.668 0.083 21.17% 10.99% 2134 0.1106
2011 0.1185 0.330 0.306 0.252 0.336 0.357 0.693 0.104 22.34% 10.59% 2234 0.1173
2012 0.1550 0.355 0.331 0.279 0.363 0.433 0.796 0.154 23.15% 11.25% 2435 0.1506
2013 0.1120 0.325 0.312 0.251 0.335 0.331 0.666 0.081 22.99% 10.76% 2398 0.1103
2014 0.1103 0.329 0.293 0.246 0.332 0.333 0.665 0.087 20.13% 10.93% 2106 0.1091
2015 0.1162 0.327 0.301 0.250 0.334 0.340 0.673 0.090 21.34% 10.81% 2146 0.1111
Total 0.1199 0.332 0.308 0.254 0.338 0.352 0.690 0.098 21.84% 10.88% 13417 0.1168
Comparing Hittersin Different Environments (LeagueAverages)
Now that we’ve determined the best offensive metrics for determining production (OPS and
wRC), we can now move into converting those metrics so that we are able to compare players
across different run scoring environments.
Let’s say you are trying to compare Player A, who played in the NECBL in 2015 and Player B
who played in the NECBL in 2012. You cannot simply compare their wOBAs or their OPS.
These two seasons had drastic differences in offensive environments. Below are the total
statistics and league averages for the NECBL from 2010-2015.
Offense as a whole was way up during the 2012 season. Apparently the league instituted a
different type of baseball and it was reported that it increased the offense. However, the stats
for that year were actually so inflated that I doubt the baseball was the entire cause. It could
have attributed to the spike slightly, but it is more likely that during that year the league just
simply had poor pitchers and superior hitters.
So you can tell that if you went to compare a player that played during 2012 to a player that
played during 2015, the basis of doing so would not be equal. A player with a wOBA of .350 in
2012 and a player with a wOBA of .350 in 2015 would not be equivalent.
This is the same when comparing players across collegiate conferences or across different levels
of baseball. Analyzing their numbers relative to their respective league averages would be the
only way to compare players across different run scoring environments.

9 | P a g e
Park Team Years worth of data PF - R
Rogers Park Danbury 6 117
Alumni Field Keene 6 112
Robbie Mills Field Laconia 6 110
Cardines Field Newport 6 108
Mackenzie Stadium Valley 6 99
Montpelier Recreation Field Vermont 6 99
Old Mountain Field Ocean State 3 96
Fitsch Senior High Mystic 5 94
Joe Wolfe Field North Adams 6 94
Forges Field Plymouth 3 90
Paul Walsh Field New Bedford 6 89
Goodall Park Sanford 5 86
Park Factors
Comparing players over different environments can become more accurate by implementing
park factors. Park factors are a way of evening out the playing field. As we know, some
stadiums are hitter friendly and some are pitcher friendly. By implementing park factors we
give a little boost to players who play in stadiums that generally suppress runs and slightly
punish players who play in stadiums that tend to produce more runs.
The chart below shows the park factors for all NECBL Parks. All factors below 100 are pitcher
friendly parks and factors above 100 are hitterfriendly parks.
There are complex ways to calculate park factors, but the simplest way is to take the total
number of runs per game in that particularpark and divide it by the total number of runs per
game in all other parks. For example – Over the past 6 years, Rogers Park has averaged 10.817
runs per game. In that same period, all other parks averaged 9.131 runs per game. 10.817/9.131
= 1.184. We call this 1.184 the initial park factor,or iPF. Then we apply regression to this factor.
I used weights found on a blog site where the blogger cited the source of the weights as being
from baseballboards.com. These weights do seem arbitrary, but they fit because they’re based
on the number of years worth of data you are using. The final park factor formula is as follows
1-(1-iPF)*X
X = .6 for 1 year
X = .7 for 2 years
X = .8 for 3 years
X = .9 for 4+ years
The final formula for Rogers Park is below.
1-(1-1.184)*.9 = 1.166
That 1.166 gets rounded to 1.17 and put in better reading terms where 1 = 100. So the final PF is
117.

10 | P a g e
wRC+ and OPS+
The wRC+ and OPS+ metrics are used to do just what was explained above – compare players
across various run scoring environments. These statistics make everything relative by
incorporating park factors and league averages. While wRC and OPS are value statistics, wRC+
and OPS+ are considered rate statistics in which the league average will always be 100.
Everything above 100 is above league average and everything below 100 is below league
average. These metrics are calculated as follows.
𝑤𝑅𝐶+ =
(
(
𝑤𝑅𝐴𝐴
𝑃𝐴
+
𝐿𝑒𝑎𝑔𝑢𝑒𝑅𝑢𝑛𝑠
𝑃𝐴
)+ (
𝑃𝐴
− (𝑃𝑎𝑟𝑘𝐹𝑎𝑐𝑡𝑜𝑟 ∗
𝑃𝐴
))
𝐿𝑒𝑎𝑔𝑢𝑒𝑤𝑅𝐶
𝑃𝐴
)
∗ 100
𝑂𝑃𝑆+ = (
100 ∗ (
𝑂𝐵𝑃
𝐿𝑒𝑎𝑔𝑢𝑒𝑂𝐵𝑃
+
𝑆𝐿𝐺
𝐿𝑒𝑎𝑔𝑢𝑒𝑆𝐿𝐺
) − 1
𝑃𝑎𝑟𝑘 𝐹𝑎𝑐𝑡𝑜𝑟
)
By calculating these metrics, one can now compare players over different time periods and
different league because they are relative to their specific league averages.
After calculating both of these metrics, I scaled them to make sure the league average is 100.

11 | P a g e
LeagueFindings – Offense
I have calculated all NECBL players’ wRC+ and OPS+ from 2010-2015. As a result we can see
some trends on the surface.
The charts above show and prove that the older a player is, and the more collegiate experience a
hitter has coming into the NECBL season, the better they perform offensively.
92 94
100
105
80
90
100
110
120
Frosh Soph Junior Senior
Average wRC+
90
95
99
105
80
90
100
110
120
Average OPS+
Total

12 | P a g e
Then we can look at what level players come from and which perform the best offensively in
the NECBL.
The only interesting part of this is that in terms of wRC+, Division II players seem to
outperform Division I players. I think that this is the case only because there are far more
Division I players than Division II players that have played in this league. This chart shows
data from 695 Division I players and only 61 Division II players. If there were more data
available for Division II players, I’d imagine their wRC+ would decrease – probably to below
league average
However, we can only interpret the data we have available.
101
104
93
87
101
96
89
87
80
90
100
110
120
NCAA D-I NCAA D-II NCAA D-III NJCAA
NECBL 2010-2015
Average of wRC+
Average of OPS+

13 | P a g e
The charts below show the overall offensive production by team in the NECBL over the past 6
years.
Below are the Standard Deviation values for each team’s average wRC+. As you can see, while
Newport’s and Mystic’s average wRC+ are the same at 104,Newport has a lower standard
deviation. You can interpret this as while their averages are the same (above average offensive
production at the same rate), Newport has been more consistent offensively than Mystic.
Team St Dev wRC+
Newport 23.61
Plymouth 24.66
Valley 24.84
NorthAdams 25.43
Sanford 26.31
Laconia 27.24
Danbury 28.90
Mystic 29.15
Keene 29.60
Ocean State 29.89
Vermont 30.50
New Bedford 32.60
85
82
94
104
98
104 105
110 112
117
91
106
75
80
85
90
95
100
105
110
115
120
125
NECBL Teams wRC+, 2010-2015
Total

14 | P a g e
You can make the same interpretation when looking at the OPS+ standard deviation values.
While Newport and Ocean State have both been tops in the league in OPS+ over the past 6
seasons, Newport has been much more consistent than Ocean State.
Team St Dev OPS+
Danbury 34.73
NorthAdams 36.26
Keene 36.95
Newport 37.59
Laconia 37.84
Valley 39.72
Plymouth 42.31
Sanford 44.02
Vermont 45.28
OceanState 47.03
New Bedford 47.80
Mystic 48.98
One thing you might also notice is that the standard deviations for OPS+ are higher than the
values for wRC+. This indicates the volatility of wRC+ is not as drastic as OPS+ as a general
statistic.
93 94
99 98
86
114
92
114
106
104
101 99
75
80
85
90
95
100
105
110
115
120
125
NECBL Teams OPS+, 2010-2015
Total

15 | P a g e
Conference Level # of Players Average wRC+
Sun Belt NCAA D-I 7 129
Ivy NCAA D-I 68 117
Sunshine State NCAA D-II 13 115
WCC NCAA D-I 8 115
Patriot NCAA D-I 12 112
America East NCAA D-I 39 111
AAC NCAA D-I 8 109
Big West NCAA D-I 11 109
Big Ten NCAA D-I 25 108
MAAC NCAA D-I 43 108
Ohio Valley NCAA D-I 12 106
Big East NCAA D-I 15 102
Conference USA NCAA D-I 25 102
Northeast NCAA D-I 30 102
Atlantic 10 NCAA D-I 42 101
Atlantic Sun NCAA D-I 14 101
ACC NCAA D-I 71 99
CAA NCAA D-I 20 99
Northeast 10 NCAA D-II 29 98
Pac 12 NCAA D-I 58 98
Big 12 NCAA D-I 19 97
MWC NCAA D-I 7 96
SEC NCAA D-I 59 95
Southern NCAA D-I 10 93
Little East NCAA D-III 8 91
MAC NCAA D-I 11 87
NJCAA NJCAA 35 87
Conference Standard Deviation-wRC+
AAC 12.4
Atlantic Sun 15.3
Big East 22.2
America East 24.4
CAA 25.2
Big Ten 25.4
Sun Belt 25.5
Pac 12 26.2
Northeast 10 26.3
MWC 26.4
Big 12 26.5
NJCAA 26.5
Sunshine State 27.4
MAAC 27.5
WCC 28.8
MAC 29.1
Southern 29.5
Northeast 30
ACC 30.2
Ivy 30.3
Atlantic 10 31.7
Conference USA 31.8
Ohio Valley 32.5
Big West 32.8
SEC 33.3
Patriot 34
Little East 57
Collegiate ConferenceFindings - Offense
The way teams in the NECBL acquire players, or decide on which players they want to sign, is
highly statistical. Most decisions are based on recommendations from collegiate coaches and a
statistical review of the players’ performances in their spring seasons. Having a more advanced
statistical look at which conferences produce the best players is a valuable advantage in a
league such as this one.
Below is a chart of the average wRC+ in the NECBL by the represented conference.
Obviously some of these numbers have to be taken with a grain of salt and that is why I
included the number of players that were analyzed from each conference. You can assume that
as more players from the Sun Belt play in the NECBL, their wRC+ will regress back towards
league average.
However, some of the conferences with a large amount of data are where some of the more
interesting findings come to fruition. The more players analyzed in the conference,the
stronger, more valid, and more representative of the true talent level the wRC+ statistic actually
is.

16 | P a g e
Conference Level # of Players Average OPS+
WCC NCAA D-I 8 124
Ivy NCAA D-I 68 120
AAC NCAA D-I 8 109
SEC NCAA D-I 59 101
ACC NCAA D-I 71 98
CAA NCAA D-I 20 97
MAC NCAA D-I 11 93
NJCAA NJCAA 35 87
MWC NCAA D-I 7 74
Conference Standard Deviation-OPS+
Southern 31.3
Big East 34.4
Atlantic Sun 34.6
NJCAA 35.3
MAAC 35.8
Big 12 35.9
Northeast 10 36
America East 36.2
Pac 12 37
AAC 37.3
MAC 39
CAA 39.3
Sun Belt 39.9
Northeast 39.9
Big West 40.6
SEC 41.1
Sunshine State 41.5
Big Ten 42.2
Atlantic 10 42.2
Ivy 45.8
ACC 47.4
Ohio Valley 48
Conference USA 51.6
WCC 57
Patriot 59.4
MWC 59.6
Little East 62.4
Take a look at the Ivy League for example. There is a lot of data here – 68 Ivy League position
players have played in the league over the past 6 years. Actually – probably more have played
in the league during this time, but 68 of them met the criteria for minimum number of at bats
(which I set at 50). The average Ivy League position player in the NECBL boasts a wRC+ of 117.
Some other strong sets of data include the America East Conference,the MAAC, andeven the
Big Ten as generally representing above average players in the NECBL. You can also conclude
that players from NJCAA are likely to be below average offensive performers in the summer
league.
Looking at the standard deviations for these conferences’ wRC+is valuable as it shows how
consistent and how much volatility these numbers have.
As we look at the strong data sets that I mentioned before, we see that the America East
Conference and the Big Ten are far more consistent and less volatile than the Ivy League.
Below is the same analysis for the OPS+ statistic.
You can generally see the same conferences stand out with this statistic too.

17 | P a g e
Conference Level # of Players Average wRC+
MAC NCAA D-I 11 118
Ivy NCAA D-I 24 112
Patriot League NCAA D-I 10 110
CAA NCAA D-I 16 107
ACC NCAA D-I 42 92
SEC NCAA D-I 26 90
Conference Standard Deviation-wRC+
Ohio Valley 14
Big East 16.6
Sunshine State 17
Pac 12 18.7
Ivy 20
Conference USA 20.1
ACC 20.6
Southern 21.3
Big Ten 21.7
CAA 22.1
Patriot League 22.5
Big West 22.9
MAAC 23
MAC 23.8
Atlantic Sun 24.1
SEC 24.3
Big 12 25.6
America East 26
Atlantic 10 27.3
Little East 27.8
Northeast 10 28.2
Northeast 28.4
While this data has some substance to it, it does tell us limited information. For example, just
looking at this data would you recommend taking every single player from the Ivy League that
becomes available? That’s just not a reasonable conclusion. What if every player the NECBL
received from the Ivy League over the years were just very good players? In order to take this
analysis further, I had to calculate each of these players’ wRC+ and OPS+ from their collegiate
seasons prior to their NECBL season. This gave a snap shot of what type of player each
conference was sending to the NECBL prior to their summer performance. One thing to note
that when calculating these metrics I could not include Park Factors since there was just not
enough information for me to do so. So these metrics are solely based on league averages and
the linear weights derived from their respective conferences’ year.
The results are below.
So to reiterate what you are looking at here, this is the average wRC+ of all NECBL players’
spring seasons with their respective colleges – sorted by conference. This tells us that –
generally speaking – the players the NECBL receives from the SEC perform to an average
wRC+ of 90 in the Spring prior to joining the NECBL. This makes sense when you think about
it because above average players in bigger conferences, such as the SEC, ACC,and Pac 12,will
normally play in the Cape Cod League, or get drafted.

18 | P a g e
Conference Level # of Players Average OPS+
MAC NCAA D-I 11 131
Ivy NCAA D-I 24 121
CAA NCAA D-I 16 112
SEC NCAA D-I 26 93
ACC NCAA D-I 42 91
Conference Standard Deviation-OPS+
Ohio Valley 25.1
Conference USA 26.6
Sunshine State 27.2
ACC 28.1
Ivy 29.4
Pac 12 29.5
Big Ten 29.7
CAA 33.3
Big 12 34.5
SEC 35.2
Big East 35.3
Southern 38
Atlantic Sun 38.6
MAAC 39
Big West 39.4
MAC 40.6
Patriot League 43.4
America East 43.8
Atlantic 10 45.4
Northeast 47
Northeast 10 49.5
Little East 56.3
On the other end of the spectrum, the Little East, a Division III conference, generally sends the
NECBL their best offensive players – with an average wRC+ of 129.
It also makes sense that more conferences than not are above average because the better players
usually play in summer leagues such as the NECBL while worse players may not be of interest
to top summer league teams.
I also used a minimum of 50 at bats for a player to qualify for this analysis. As you can see, not
many players from the bigger conferences qualified during their spring seasons. For example,
out of 71 ACC players who had a minimum of 50 at bats in the NECBL, only 42 of them
qualified during their spring seasons. Many of them were bench players.
The OPS+ and standard deviation results are below.

19 | P a g e
Performance ImprovementRating – Hitters
Below is a chart representing what I call each conference’s Performance ImprovementRating
(PIR). This simply represents the average wRC+/OPS+ from the NECBL season divided by the
average wRC+/OPS+ of the spring season. This essentially tells us which conferences improve
the most over these two seasons based on these two rate statistics. Any value greater than 1
shows improvement from spring to NECBL and any value less than 1 shows a decline in
performance. This statistic in a way shows which conferences across the country match the
talent level of the NECBL as a whole. Any value close to 1 could potentially represent an even
talent level. You can look at it as any value above 1 indicates a conference that has better
offensive players than the NECBL has, and values below 1 indicates the conference has worse
offensive players
Performance Improvement Rating
Conference wRC+ PIR
ACC 1.076
Conference USA 1.074
Big Ten 1.059
SEC 1.056
Pac 12 1.054
Ivy 1.045
Sunshine State 1.036
Big West 1.019
Patriot League 1.018
Big 12 1.000
America East 0.991
Atlantic Sun 0.927
Atlantic 10 0.927
CAA 0.925
Northeast 0.919
MAAC 0.915
Southern 0.903
Big East 0.895
Ohio Valley 0.883
Northeast 10 0.810
MAC 0.737
Little East 0.705

20 | P a g e
Performance Improvement Rating
Conference OPS+ PIR
Pac 12 1.118
SEC 1.086
ACC 1.077
Conference USA 1.000
Ivy 0.992
Big Ten 0.981
Big 12 0.981
Big West 0.946
America East 0.891
Atlantic Sun 0.889
Southern 0.878
CAA 0.866
Northeast 0.846
Atlantic 10 0.837
MAAC 0.793
Ohio Valley 0.784
Big East 0.722
MAC 0.710
Northeast 10 0.692
Little East 0.677
As you can see, players from the ACC, Pac 12, and SEC perform better in the NECBL than they
did in their respective conferences. This stat is confirming that the offensive talent in those
conferences is better than the offensive talent in the NECBL. Players from the Northeast 10 and
Little East Conferences (Division II and Division III respectively) perform much worse than they
do in their spring seasons – indicating those conferences maintain worse offensive talent than
the NECBL.

21 | P a g e
Predicting Performance -Hitters
So what does this all mean? Revisiting the question posed earlier, should an NECBL team take
all players from the Ivy League that become available? Or has the Ivy League just sent the
NECBL superb players over the years? Well that was somewhat answered by determining the
average player the Ivy League sends to the NECBL has a wRC+ of 112 and then plays to an
average wRC+ of 117 in the NECBL.
So now we can take this analysis to the next level and try to see which conferences’ players
performances correlate over these two seasons and if we can predict a player’s performance in
the NECBL based on their performance in their respective conference with some statistical
significance.
To do this I ran regressions on all qualified players (min 50 AB’s in both spring and summer)
and analyzed the results.
The following conferences’ results are statistically insignificant for both wRC+ and OPS+
statistics
 Big 12
 Big East
 Big West
 CAA
 Conference USA
 MAAC
 Pac 12
 SEC
 Southern
These conferences prove to have no predictive capabilities for wRC+ and OPS+.
There are 3 conferences that proved to have a statistically significant relationship with a 99%
confidence level – for both wRC+ and OPS+. Those conferences are below.
 America East Conference
 Northeast 10 Conference
 Patriot League
The rest of the conferences that have statistically significant correlation are either of 90% or 95%
confidence – I indicate the confidence level on each conference’s analysis page.
Below are all analyses of the significant conferences – this includes a scatter plot, a regression
analysis, and a prediction table. All scatter plots are set up as to show the relationship between
players’ wRC+ in their spring season and wRC+ in their NECBL season. The graphs show the
rate at which wRC+ increases in the NECBL as wRC+ increases in the spring. The X axis is the
number of players analyzed (essentially Player #1, Player #2, etc) and the Y axis is the wRC+.
The first conferences analyzed are the 99% confidence levels.

22 | P a g e
America East Conference Analysis– 99% confidence forwRC+ and
OPS+
By using the formula y = mx + b, where y = the NECBL wRC+, we can now predict how a
player will translate from the America East Conference to the NECBL. For example…
If a player from the America East Conference has a wRC+of 100 in the spring…
Y = (100 * .439393252) + 61.42871876
Y = 105.37
Then he will translate to a wRC+ of 105.37 in the NECBL, and like mentioned before, we can
state this with a 99% confidence level. The confidence level is based on whether the model is
0
20
40
60
80
100
120
140
160
180
200
0 10 20 30 40
wRC+
# of Players
America East wRC+
Spring wRC+
NECBL wRC+
Linear (Spring wRC+)
Linear (NECBL wRC+)
SUMMARY OUTPUT
R Square 0.225822365
Observations 35
ANOVA
Regression 1 4426.476951 4426.476951 9.625876188 0.003916319
Residual 33 15175.11098 459.8518478
Total 34 19601.58793
Intercept 61.42871876 16.27374467 3.774713196 0.000634685 28.31953645 94.53790108 28.31953645 94.53790108
Spring wRC+ 0.439393252 0.141622823 3.102559619 0.003916319 0.151259454 0.727527051 0.151259454 0.727527051

23 | P a g e
statistically significant. You can see this by looking at the P-value. If the P-value is less than .1
you can say you are 90% confident in the model. If it is less than .05 you can say you are 95%
confident, and if it is less than .01,you can say you are 99% confident.
Looking at the R Squared is of secondary significance, but it still is useful to look at. What the R
squared shows is how closely the data fits to the regression line. The closer the R squared is to
1, the more the model explains the variability of the data around the mean.
The chart below shows the expected NECBL wRC+ for America East Conference players.
Below is the same analysis for the America East in terms of OPS+.
America East wRC+ Expected NECBL wRC+
50 83.40
60 87.79
70 92.19
80 96.58
90 100.97
100 105.37
110 109.76
120 114.16
130 118.55
140 122.94
150 127.34
0
20
40
60
80
100
120
140
160
180
200
0 10 20 30 40
OPS+
# of Players
America East OPS+
Spring OPS+
NECBL OPS+
Linear (Spring OPS+)
Linear (NECBL OPS+)

24 | P a g e
SUMMARY OUTPUT
R Square 0.221179799
Observations 35
ANOVA
Regression 1 10288.3273 10288.3273 9.371782284 0.00435928
Residual 33 36227.34614 1097.798368
Total 34 46515.67344
Intercept 58.17510044 16.45617037 3.535154239 0.001231311 24.69477026 91.65543063 24.69477026 91.65543063
Spring OPS+ 0.397508998 0.129848181 3.061336683 0.00435928 0.13333089 0.661687106 0.13333089 0.661687106
America East OPS+ Expected NECBL OPS+
50 78.05
60 82.03
70 86.00
80 89.98
90 93.95
100 97.93
110 101.90
120 105.88
130 109.85
140 113.83
150 117.80

25 | P a g e
Northeast10 Analysis – 99% confidence for wRC+ and OPS+
0
50
100
150
200
0 5 10 15 20
wRC+
# of Players
Northeast 10 wRC+
Spring wRC+
NECBL wRC+
Linear (NECBL wRC+)
SUMMARY OUTPUT
R Square 0.425446469
Observations 19
ANOVA
Regression 1 5301.966947 5301.966947 12.58819167 0.002473
Residual 17 7160.157746 421.1857498
Total 18 12462.12469
Coefficients Standard Error t Stat P-value Lower 95%Upper 95%Lower 95.0%Upper 95.0%
Intercept 26.05768957 21.26776332 1.225220028 0.237197533 -18.8134 70.92875 -18.8134 70.92875
Spring wRC+0.608512094 0.171509247 3.54798417 0.0024728 0.246659 0.970365 0.246659 0.970365
Northeast 10 wRC+ Expected NECBL wRC+
50 56.48
60 62.57
70 68.65
80 74.74
90 80.82
100 86.91
110 92.99
120 99.08
130 105.16
140 111.25
150 117.33

26 | P a g e
0
50
100
150
200
250
0 5 10 15 20
OPS+
# of Players
Northeast 10 OPS+
Spring OPS+
NECBL OPS+
Linear (NECBL OPS+)
SUMMARY OUTPUT
R Square 0.362015227
Observations 19
ANOVA
Regression 1 10991.42501 10991.42501 9.646403994 0.006423
Residual 17 19370.35037 1139.432375
Total 18 30361.77538
Intercept 24.20419885 22.31680866 1.084572584 0.293253183 -22.8802 71.28855 -22.8802 71.28855
Spring OPS+ 0.499069765 0.160686184 3.105866062 0.006423 0.160052 0.838088 0.160052 0.838088
Northeast 10 OPS+ Expected NECBL OPS+
50 49.16
60 54.15
70 59.14
80 64.13
90 69.12
100 74.11
110 79.10
120 84.09
130 89.08
140 94.07
150 99.06

27 | P a g e
Patriot LeagueAnalysis – 99% confidence for wRC+ and OPS+
0
50
100
150
200
0 2 4 6 8 10 12
wRC+
# of Players
Patriot League wRC+
Spring wRC+
NECBL wRC+
Linear (NECBL wRC+)
R Square 0.61644
Adjusted R Square0.568493546
Standard Error22.33090552
Observations 10
ANOVA
Regression 1 6411.472481 6411.472481 12.85716196 0.007129
Residual 8 3989.354729 498.6693411
Total 9 10400.82721
Intercept -17.4 36.88265754 -0.471769507 0.649690638 -102.452 67.65145 -102.452 67.65145
Spring wRC+ 1.1849 0.330453518 3.585688491 0.007129 0.422876 1.946931 0.422876 1.946931
Patriot League wRC+ Expected NECBL wRC+
50 41.85
60 53.69
70 65.54
80 77.39
90 89.24
100 101.09
110 112.94
120 124.79
130 136.64
140 148.49
150 160.34

28 | P a g e
0
50
100
150
200
0 2 4 6 8 10 12
OPS+
# of Players
Patriot League OPS+
Spring OPS+
NECBL OPS+
Linear (NECBL OPS+)
R Square 0.71451
Observations 10
ANOVA
Regression 1 22715.67472 22715.67472 20.02223196 0.002071
Residual 8 9076.180824 1134.522603
Total 9 31791.85555
Intercept -34.249 33.40797258 -1.02517595 0.335277014 -111.288 42.78987 -111.288 42.78987
Spring OPS+ 1.15841 0.258885083 4.474620873 0.00207 0.561423 1.755403 0.561423 1.755403
Patriot League OPS+ Expected NECBL OPS+
50 23.67
60 35.26
70 46.84
80 58.42
90 70.01
100 81.59
110 93.18
120 104.76
130 116.34
140 127.93
150 139.51

29 | P a g e
ACC Analysis – 90% confidence forwRC+ and OPS+
0
20
40
60
80
100
120
140
160
180
0 10 20 30 40
wRC+
# of Players
ACC wRC+
Spring wRC+
NECBL wRC+
Linear (NECBL
wRC+)
SUMMARY OUTPUT
R Square 0.084
Observations 43
ANOVA
Regression 1 2505.515 2505.515 3.761410775 0.05935
Residual 41 27310.52 666.1104
Total 42 29816.04
CoefficientsStandard Error t Stat P-value Lower 95%Upper 95%Lower 95.0%Upper 95.0%
Intercept 66.853 18.27236 3.658677 0.000716177 29.95089 103.7545 29.95089 103.7545
X Variable 1 0.3746 0.193143 1.939436 0.059350128 -0.01547 0.76465 -0.01547 0.76465
ACC wRC+ Expected NECBL wRC+
50 85.58
60 89.33
70 93.07
80 96.82
90 100.57
100 104.31
110 108.06
120 111.80
130 115.55
140 119.30
150 123.04

30 | P a g e
0
50
100
150
200
250
0 10 20 30 40
OPS+
# of Players
ACC OPS+
Spring OPS+
NECBL OPS+
Linear (Spring
OPS+)
Linear (NECBL
OPS+)
SUMMARY OUTPUT
R Square 0.0852
Observations 43
ANOVA
Regression 1 6991.502 6991.502 3.819334372 0.057508
Residual 41 75052.76 1830.555
Total 42 82044.26
CoefficientsStandard Error t Stat P-value Lower 95%Upper 95%Lower 95.0%Upper 95.0%
Intercept 57.071 22.45637 2.541439 0.014917557 11.71993 102.423 11.71993 102.423
Spring ERA 0.4591 0.234924 1.954312 0.057507727 -0.01532 0.933555 -0.01532 0.933555
ACC OPS+ Expected NECBL OPS+
50 80.03
60 84.62
70 89.21
80 93.80
90 98.39
100 102.98
110 107.57
120 112.17
130 116.76
140 121.35
150 125.94

31 | P a g e
Atlantic 10 Analysis – 90% confidence forwRC+ and 95% confidence for
OPS+
0
50
100
150
200
0 10 20 30 40
wRC+
# of Players
Atlantic 10 wRC+
Spring wRC+
NECBL wRC+
Linear (Spring
wRC+)
Linear (NECBL
wRC+)
SUMMARY OUTPUT
R Square 0.0966
Observations 39
ANOVA
Regression 1 3869.63498 3869.63498 3.958265526 0.054071774
Residual 37 36171.52344 977.6087418
Total 38 40041.15843
Intercept 59.241 20.75949348 2.853674946 0.007038245 17.17811754 101.3035753 17.17811754 101.3035753
Spring wRC+ 0.3693 0.185598663 1.989539023 0.0541 -0.006802826 0.74531439 -0.006802826 0.74531439
Atlantic 10 wRC+ Expected NECBL wRC+
50 77.70
60 81.40
70 85.09
80 88.78
90 92.47
100 96.17
110 99.86
120 103.55
130 107.24
140 110.94
150 114.63

32 | P a g e
0
50
100
150
200
250
0 10 20 30 40
OPS+
# of Players
Atlantic 10 OPS+
Spring OPS+
NECBL OPS+
Linear (NECBL OPS+)
SUMMARY OUTPUT
R Square 0.1292
Observations 39
ANOVA
Regression 1 9353.907994 9353.907994 5.489582626 0.024615741
Residual 37 63045.70299 1703.937919
Total 38 72399.61098
Intercept 56.417 18.35423712 3.073767488 0.003956585 19.22744069 93.60587395 19.22744069 93.60587395
Spring OPS+ 0.3454 0.147398325 2.342985836 0.0246 0.046694815 0.644009562 0.046694815 0.644009562
Atlantic 10 OPS+ Expected NECBL OPS+
50 73.68
60 77.14
70 80.59
80 84.04
90 87.50
100 90.95
110 94.41
120 97.86
130 101.31
140 104.77
150 108.22

33 | P a g e
Atlantic Sun Analysis – 90% confidence for OPS+
0
50
100
150
200
0 5 10 15
OPS+
# of Players
Atlantic Sun OPS+
Spring OPS+
NECBL OPS+
Linear (NECBL OPS+)
SUMMARY OUTPUT
R Square 0.28725
Observations 12
ANOVA
Regression 1 3520.125867 3520.125867 4.030171054 0.072472779
Residual 10 8734.432905 873.4432905
Total 11 12254.55877
Intercept 56.0138 28.24289665 1.983286399 0.075459011 -6.915342256 118.9428478 -6.915342256 118.9428478
Spring OPS+ 0.46327 0.230765859 2.007528594 0.072473 -0.050909314 0.977447436 -0.050909314 0.977447436
Atlantic Sun OPS+ Expected NECBL OPS+
50 79.18
60 83.81
70 88.44
80 93.08
90 97.71
100 102.34
110 106.97
120 111.61
130 116.24
140 120.87
150 125.50

34 | P a g e
Big Ten Analysis– 90% confidence for wRC+
0
20
40
60
80
100
120
140
160
180
0 5 10 15 20
wRC+
# of Players
Big Ten wRC+
Spring wRC+
NECBL wRC+
Linear (Spring
wRC+)
Linear (NECBL
wRC+)
SUMMARY OUTPUT
R Square 0.1912
Observations 18
ANOVA
Regression 1 2113.37406 2113.37406 3.782150768 0.069595
Residual 16 8940.411693 558.7757308
Total 17 11053.78575
Intercept 54.328 27.43772312 1.9800333 0.065165865 -3.83777 112.493 -3.83777 112.493
Spring wRC+ 0.5138 0.264212888 1.944775249 0.0696 -0.04627 1.073941 -0.04627 1.073941
Big 10 wRC+ Expected NECBL wRC+
50 80.02
60 85.16
70 90.30
80 95.43
90 100.57
100 105.71
110 110.85
120 115.99
130 121.13
140 126.26
150 131.40

35 | P a g e
Ivy LeagueAnalysis– 95% confidence for wRC+
0
20
40
60
80
100
120
140
160
180
0 5 10 15 20 25
wRC+
# of Players
Ivy League wRC+
Spring wRC+
NECBL wRC+
Linear (NECBL wRC+)
SUMMARY OUTPUT
R Square 0.2445
Observations 24
ANOVA
Regression 1 4065.864957 4065.864957 7.120844019 0.014035
Residual 22 12561.5768 570.9807638
Total 23 16627.44176
Intercept 38.299 28.20544029 1.357851577 0.188275616 -20.1957 96.7933 -20.1957 96.7933
Spring wRC+ 0.6634 0.248594379 2.668490963 0.014035 0.147819 1.178925 0.147819 1.178925
Ivy League wRC+ Expected NECBL wRC+
50 71.47
60 78.10
70 84.73
80 91.37
90 98.00
100 104.64
110 111.27
120 117.90
130 124.54
140 131.17
150 137.80

36 | P a g e
MAC Analysis – 95% confidence forOPS+
0
50
100
150
200
250
0 2 4 6 8 10 12
OPS+
# of Players
MAC OPS+
Spring OPS+
NECBL OPS+
Linear (NECBL OPS+)
SUMMARY OUTPUT
R Square 0.4441
Observations 11
ANOVA
Regression 1 6769.718238 6769.718238 7.18969743 0.02515
Residual 9 8474.273739 941.5859709
Total 10 15243.99198
Intercept 8.6126 32.73573416 0.263095481 0.79840103 -65.4408 82.666 -65.4408 82.666
Spring OPS+ 0.6404 0.238843795 2.681361116 0.0251 0.100124 1.180729 0.100124 1.180729
MAC OPS+ Expected NECBL OPS+
50 40.63
60 47.04
70 53.44
80 59.85
90 66.25
100 72.66
110 79.06
120 85.46
130 91.87
140 98.27
150 104.68

37 | P a g e
Ohio Valley Analysis – 95% confidence for wRC+ and 90% confidence
for OPS+
0
50
100
150
200
0 2 4 6 8 10 12
wRC+
# of Players
Ohio Valley wRC+
Spring wRC+
NECBL wRC+
Linear (NECBL wRC+)
SUMMARY OUTPUT
R Square 0.4623
Observations 11
ANOVA
Regression 1 3719.79904 3719.79904 7.737633432 0.021343
Residual 9 4326.670636 480.7411818
Total 10 8046.469676
Intercept -54.51 59.97828828 -0.908908852 0.387090126 -190.195 81.16552 -190.195 81.16552
Spring wRC+ 1.3779 0.495340966 2.781660193 0.02134 0.257331 2.498409 0.257331 2.498409
Ohio Valley wRC+ Expected NECBL wRC+
50 14.38
60 28.16
70 41.94
80 55.71
90 69.49
100 83.27
110 97.05
120 110.83
130 124.61
140 138.39
150 152.17

38 | P a g e
0
20
40
60
80
100
120
140
160
180
200
0 2 4 6 8 10 12
OPS+
# of Players
Ohio Valley OPS+
Spring OPS+
NECBL OPS+
Linear (NECBL OPS+)
SUMMARY OUTPUT
R Square 0.291
Observations 11
ANOVA
Regression 1 4459.359155 4459.359155 3.693368631 0.086804
Residual 9 10866.56557 1207.396174
Total 10 15325.92472
Intercept 0.9138 61.65121189 0.014822593 0.988497101 -138.551 140.3786 -138.551 140.3786
Spring OPS+ 0.8400 0.437091266 1.921813891 0.0868 -0.14876 1.828777 -0.14876 1.828777
Ohio Valley OPS+ Expected NECBL OPS+
50 42.91
60 51.31
70 59.71
80 68.11
90 76.51
100 84.91
110 93.31
120 101.71
130 110.11
140 118.51
150 126.92

39 | P a g e
Sunshine State Analysis – 90% confidence for OPS+
0
50
100
150
200
250
0 2 4 6 8 10 12 14
OPS+
# of Players
Sunshine State OPS+
Spring OPS+
NECBL OPS+
Linear (NECBL OPS+)
SUMMARY OUTPUT
R Square 0.23012
Observations 13
ANOVA
Regression 1 4753.612221 4753.612221 3.287908828 0.097141
Residual 11 15903.64489 1445.785899
Total 12 20657.25711
Intercept 29.3346 46.98710443 0.624312167 0.545153163 -74.0833 132.7525 -74.0833 132.7525
Spring OPS+ 0.73175 0.403553551 1.813259173 0.09714 -0.15647 1.619963 -0.15647 1.619963
Sunshine State OPS+ Expected NECBL OPS+
50 65.92
60 73.24
70 80.56
80 87.87
90 95.19
100 102.51
110 109.83
120 117.14
130 124.46
140 131.78
150 139.10

40 | P a g e
ERA-and FIP-
ERA- and FIP- are the wRC+ and OPS+ versions for pitchers. Pitching metrics are naturally
limited outside of MLB because there’s no readily available information for GB%, LD%, and
FB%. These percentages are usually useful metrics to look at when evaluating a pitcher.
You may have seen ERA+ before and not ERA-. ERA+ is shown a lot on the MLB network and
is used by baseballreference.com. There is a slight difference between the two. In its most
simple explanation, ERA- takes a pitcher’s ERA and compares it to league average – with 100
being league average. Anything greater than 100 is below league average and anything less
than 100 is above league average. ERA+ tells you almost the same thing – just inverted.
However there is one main difference in how the statistics actually read. While ERA- tells you
how much better/worse the player is than the league, ERA+ tells you how much better/worse
the league is than the player.
I feel that ERA- is superior not only for the reason mentioned above, but also simply because it’s
easier to read for the average Joe. The average baseball fan knows that the lower a pitcher’s
ERA is the better. So by using ERA- it reads the same way. Anything that can be done to make
advanced statistics easier to read for the average fan will progress the usage of these metrics.
FIP- is the same as ERA- but it’s calculated using a player’s Fielding Independent Pitching (FIP)
instead of his Earned Run Average (ERA). FIP is a metric that estimates a pitcher’s run
prevention based on events that are independent from his defense’s ability.
ERA is calculated as follows
𝐸𝑅𝐴 =
𝐸𝑎𝑟𝑛𝑒𝑑 𝑅𝑢𝑛𝑠 ∗ 9
𝐼𝑃
FIP is calculated as follows
𝐹𝐼𝑃 = (
((13 ∗ 𝐻𝑅) + (3 ∗ ( 𝐵𝐵 + 𝐻𝐵𝑃)) − (2 ∗ 𝐾)
𝐼𝑃
) + 𝐹𝐼𝑃 𝐶𝑜𝑛𝑠𝑡𝑎𝑛𝑡

41 | P a g e
The FIP constant scales the stat to match the league average ERA. The FIP constantis calculate
as follows
𝐹𝐼𝑃 𝐶𝑜𝑛𝑠𝑡𝑎𝑛𝑡 =
𝐿𝑒𝑎𝑔𝑢𝑒 𝐸𝑅𝐴 − ((13 ∗ 𝐿𝑒𝑎𝑔𝑢𝑒𝐻𝑅) + (3 ∗ ( 𝐿𝑒𝑎𝑔𝑢𝑒𝐵𝐵 + 𝐿𝑒𝑎𝑔𝑢𝑒𝐻𝐵𝑃)) − (2 ∗ 𝐿𝑒𝑎𝑔𝑢𝑒𝐾 )
𝐿𝑒𝑎𝑔𝑢𝑒 𝐼𝑃
Below are the league average FIP’s and FIP constants for the NECBL from 2010-2015 (League
average ERA=League average FIP)
ERA- is calculated as follows
𝐸𝑅𝐴− = (
𝐸𝑅𝐴 + (𝐸𝑅𝐴 − ( 𝐸𝑅𝐴 ∗ 𝑃𝑎𝑟𝑘𝐹𝑎𝑐𝑡𝑜𝑟))
𝐿𝑒𝑎𝑔𝑢𝑒 𝐸𝑅𝐴
) ∗ 100
FIP- is calculated the same way using FIP
Year FIP FIP Constants
2010 3.44 3.05
2011 3.71 3.05
2012 5.23 3.79
2013 3.50 3.20
2014 3.39 2.76
2015 3.65 3.07

42 | P a g e
LeagueFindings – Pitching
Below are the ERA- and FIP- averages for NECBL pitchers by year and level from which they
play.
While there is a clear improvement in offensive production in the NECBL as the player gets
older, pitching on the other hand isn’t as clear.
91
104
103
94
80
85
90
95
100
105
110
115
120
Average ERA-, NECBL 2010-2015
Total
93
97
93 92
80
85
90
95
100
105
110
115
120
Average FIP-, NECBL 2010-2015
Total

43 | P a g e
It is interesting that Division III pitchers have outperformed Division II pitchers. However, as
more data becomes available in the coming years, I would expect the Division III averages to
worsen.
98
106
103
109
80
85
90
95
100
105
110
115
120
Average ERA-, NECBL 2010-2015
Total
92
100
95
101
80
85
90
95
100
105
110
115
120
Average FIP-, NECBL 2010-2015
Total

44 | P a g e
Below are the average ERA- and FIP- metrics for every team in the NECBL from 2010-2015
You can see on the surface that the FIP- values are generally lower than the ERA- values. The
team that stands out to me is Newport. They have clearly been the best pitching team over the
past 6 years as a whole in the NECBL. But even they needed to get a little on the lucky side to
get there, as there ERA- is 72 and their FIP- is higher, at 79.
100
89
106 105
114
72
100 100
109 106
95 98
60
70
80
90
100
110
120
130
140
ERA-, NECBL 2010-2015
Total
90 89
95
106 104
79
99
96
105
88
82
93
60
70
80
90
100
110
120
130
140
FIP-, NECBL 2010-2015
Total

45 | P a g e
Conferences Level # of Players Average ERA-
MAC NCAA D-I 9 82
Ivy NCAA D-I 31 94
MAAC NCAA D-I 42 95
ACC NCAA D-I 55 98
SEC NCAA D-I 40 99
NJCAA NJCAA 29 109
ECC NCAA D-II 10 110
NESCAC NCAA D-III 10 118
CAA NCAA D-I 17 120
Conferences Standard Deviation- ERA-
Atlantic Sun 22.5
Southern 29.2
Big 12 29.9
MAC 37.5
Northeast 37.9
Patriot 39.7
Sun Belt 42.1
Big East 43.3
Atlantic 10 43.5
MAAC 44.4
Ivy 44.9
America East 48.9
Northeast 10 50.6
ACC 53
Little East 54.8
NJCAA 56.9
SEC 58.7
CAA 60.7
Conference USA 63
Big Ten 64.4
ECC 65.2
Sunshine State 66.7
NESCAC 69.2
Pac 12 100.9
Collegiate ConferenceFindings - Pitchers
Below is a chart of average ERA- in the NECBL by each conference
As you can see, all of the conferences that aren’t Division I are below average, besides the
Sunshine State Conference – with an average ERA- of 88.
You can see from the standard deviation values that the Pac 12 is really all over the place; so
despite their average ERA- being around league average (101), they have had some really good
performers and some really bad performers.
On the other hand, the Northeast conference – with an average ERA- of 76 and a low standard
deviation of 37.9 - means they have been on the consistent side of performing well above league
average.

46 | P a g e
Conferences Level # of Players Average FIP-
MAC NCAA D-I 9 75
MAAC NCAA D-I 42 90
Ivy NCAA D-I 31 90
SEC NCAA D-I 40 91
ACC NCAA D-I 55 96
ECC NCAA D-II 10 97
NJCAA NJCAA 29 101
CAA NCAA D-I 17 103
Conferences Standard Deviation- FIP-
Southern 17.1
Big 12 19
Atlantic Sun 19.5
Big East 23.6
Sun Belt 26.1
Ivy 26.5
Patriot 28.4
America East 28.4
Atlantic 10 28.7
Northeast 29.3
Northeast 10 29.8
CAA 30.2
ACC 30.7
MAC 32.1
SEC 34.6
MAAC 35
Conference USA 35.7
ECC 35.9
Pac 12 36.6
NJCAA 37
Big Ten 37.9
Little East 38.6
Sunshine State 40.3
NESCAC 44
Below is the average FIP- for the NECBL from each conference.
You can see that most conferences analyzed have performed above average in terms of FIP. I
would assume that most of the small conferences (not division I) that weren’t analyzed because
they didn’t have enough players to evaluate, have performed below average. However this
does have some value when comparing conference to conference.
Now by looking at the standard deviation, even though most conferences performed above
average, we can see which have been more consistent over the past 6 years
The Big East, Atlantic Sun, and the Ivy Leagues have been the most consistent conferences
when considering the amount of players analyzed.
Like mentioned before, this information is limited in a sense. What if all Big East players were
just tremendous players? How did they perform prior to joining the NECBL and performing
above league average? That is where the information gets more valuable. It is not only
important to look at which conferences have performed the best in the NECBL, but what type of
players each respective conference sends to the NECBL. So just like the hitters, here is how the
pitchers performed in their respective spring seasons just prior to their NECBL appearance.
Again, I could not include Park Factors in this data so it is all based upon league averages.

47 | P a g e
Conferences Level # of Players Average ERA-
ECC NCAA D-II 10 63
MAAC NCAA D-I 37 91
CAA NCAA D-I 15 93
Ivy NCAA D-I 23 99
MAC NCAA D-I 8 104
SEC NCAA D-I 13 104
ACC NCAA D-I 36 109
Conferences Standard Deviation- ERA-
NESCAC 18.5
Northeast 28.8
Northeast 10 28.8
ECC 30.2
MAAC 31
Southern 33.5
Atlantic 10 35.3
Atlantic Sun 37.3
Big Ten 37.6
CAA 39.2
Ivy 43.5
Big East 43.7
SEC 44.7
Sunshine State 47.2
MAC 47.4
America East 47.6
Patriot League 47.8
ACC 48.8
Sun Belt 54
Pac 12 70.2
Below is the average ERA- from each conference for their performance in the spring prior to the
NECBL.
So to put context to what you are looking at here – the average ECC player that plays in the
NECBL, has an ERA- of 63 during the spring prior to the NECBL. Just like the data for the
hitters, it makes sense that the smaller conferences are towards the top, meaning the NECBL
gets the cream of the crop from the smaller, Division II/Division III schools, and
middle/bottom of the pack from the bigger conferences.
At first glance you can see that not only has the NECBL gotten the best of the best talent from
the smaller conferences (NESCAC, Northeast10, ECC), but they have been consistently the best
talent as well.
On the other hand, despite receiving poor talent from the Pac 12, with an average ERA- of 141,
there is a high standard deviation, showing that some better talent has been received as well.

48 | P a g e
Conferences Level # of Players Average FIP-
ECC NCAA D-II 10 85
MAAC NCAA D-I 37 96
MAC NCAA D-I 8 98
Ivy NCAA D-I 23 102
CAA NCAA D-I 15 103
SEC NCAA D-I 13 104
ACC NCAA D-I 36 105
Conferences Standard Deviation- FIP-
Sun Belt 12.4
NESCAC 13.4
ECC 14.3
MAAC 14.6
Northeast 16.3
Northeast 10 19.9
ACC 20.6
Patriot League 20.9
Atlantic 10 21.4
SEC 22.9
Ivy 23
MAC 23.1
Southern 23.5
CAA 23.8
Sunshine State 24.2
America East 24.6
Big East 27
Atlantic Sun 28.8
Big Ten 28.8
Pac 12 40.1
Here are the same tables for FIP-, conference averages for spring seasons prior to the NECBL.
Again, same deal here. The smaller conferences are towards the top and the bigger conferences
are towards the bottom.
The Pac 12 has been inconsistent while the smaller conferences have consistently sent the
NECBL top talent.

49 | P a g e
Performance ImprovementRating -Pitching
Below is the aforementioned Performance Improvement Rating (PIR) for each conference. Since
it is better to have a lower ERA-/FIP-, I inverted the formula so that it read the same way as the
hitters. It still reads that any value greater than 1 shows improvement from spring season to
NECBL season and any value less than 1 shows a worse performance.
As you can see, the smaller conferences generally come to the NECBL and perform worse than
they had in their respective spring seasons.
Conferences ERA- PIR
Atlantic Sun 1.623
Southern 1.513
Pac 12 1.396
Sun Belt 1.373
MAC 1.268
ACC 1.112
Northeast 1.105
Ivy 1.053
SEC 1.051
Atlantic 10 0.990
MAAC 0.958
Big East 0.925
Big Ten 0.922
America East 0.920
CAA 0.775
Northeast 10 0.686
NESCAC 0.636
ECC 0.573

50 | P a g e
Below is the PIR chart for each conference’s FIP-
Most conferences improve in FIP- because most of the conferences analyzed were above
average in FIP- to begin with - in the NECBL.
Conferences FIP- PIR
Southern 1.462
Pac 12 1.375
Atlantic Sun 1.369
MAC 1.307
Big East 1.301
Big Ten 1.144
SEC 1.143
Ivy 1.133
Atlantic 10 1.133
ACC 1.094
Sun Belt 1.082
MAAC 1.067
Northeast 1.065
America East 1.059
NESCAC 1.011
CAA 1.000
Northeast 10 0.908
ECC 0.876

51 | P a g e
Predicting Performance – Pitchers
Just like I did for the hitters, I ran regressions for all conferences ERA- and FIP- to see if there
were any positive and statistically significant correlations thatcould help us predict NECBL
performances.
Just like I had set a minimum of 50 ab’s for all hitters analyzed, I had to set a minimum for
pitchers as well. This requirement was either 15 IP or 10 appearances. In a shortened season of
about 40 or so games, this seemed to be a cutoff that made sense.
Below are the conferences that were statistically insignificant for ERA- and FIP-
 ACC
 America East
 Atlantic 10
 CAA
 ECC
 MAC
 NESCAC
 Northeast
 Northeast 10
 Patriot
 SEC
 Southern
 Sunshine State
The conferences above proved to have no predictive value for ERA- and FIP-.
Next are the statistically significant conferences.

52 | P a g e
Atlantic Sun Analysis – 90% confidence for FIP-
0
50
100
150
200
250
0 5 10 15
FIP-
# of Players
Atlantic Sun FIP-
Spring FIP-
NECBL FIP-
Linear (Spring FIP-)
Linear (NECBL FIP-)
SUMMARY OUTPUT
R Square 0.24447
Observations 14
ANOVA
Regression 1 1388.505722 1388.505722 3.882934771 0.072292495
Residual 12 4291.101872 357.5918227
Total 13 5679.607595
Intercept 42.8387 21.62639216 1.980853119 0.070997416 -4.281154275 89.95856702 -4.281154275 89.95856702
FIP- 0.35936 0.182368882 1.970516372 0.072292 -0.037986792 0.756708525 -0.037986792 0.756708525
Atlantic Sun FIP- Expected NECBL FIP-
50 60.81
60 64.40
70 67.99
80 71.59
90 75.18
100 78.77
110 82.37
120 85.96
130 89.56
140 93.15
150 96.74

53 | P a g e
Big East Analysis– 90% confidence for FIP-
0
50
100
150
200
0 5 10 15
FIP-
# of Players
Big East FIP-
Spring FIP-
NECBL FIP-
Linear (NECBL FIP-)
SUMMARY OUTPUT
R Square 0.2764
Observations 12
ANOVA
Regression 1 1722.547555 1722.547555 3.81898354 0.079192
Residual 10 4510.486983 451.0486983
Total 11 6233.034538
Intercept 32.706 26.44422143 1.236810798 0.24441578 -26.2149 91.6279 -26.2149 91.6279
FIP- 0.4643 0.237594238 1.954221978 0.0792 -0.06508 0.993705 -0.06508 0.993705
Big East FIP- Expected NECBL FIP-
50 55.92
60 60.57
70 65.21
80 69.85
90 74.49
100 79.14
110 83.78
120 88.42
130 93.07
140 97.71
150 102.35

54 | P a g e
Big Ten Analysis– 95% confidence for FIP-
0
50
100
150
200
0 5 10 15 20
FIP-
# of Players
Big Ten FIP-
Spring FIP-
NECBL FIP-
Linear (NECBL FIP-)
SUMMARY OUTPUT
R Square 0.2728
Observations 19
ANOVA
Regression 1 7130.951157 7130.951157 6.375998839 0.021798
Residual 17 19012.8908 1118.405341
Total 18 26143.84196
Intercept 2.4668 34.94025448 0.070601057 0.944539165 -71.2507 76.18431 -71.2507 76.18431
FIP- 0.8379 0.331851513 2.525074026 0.0218 0.137804 1.538095 0.137804 1.538095
Big Ten FIP- Expected NECBL FIP-
50 44.36
60 52.74
70 61.12
80 69.50
90 77.88
100 86.26
110 94.64
120 103.02
130 111.40
140 119.78
150 128.16

55 | P a g e
Ivy LeagueAnalysis – 95% confidence for ERA-
0
50
100
150
200
250
300
0 5 10 15 20 25
ERA-
# of Players
Ivy League ERA-
Spring ERA-
NECBL ERA-
Linear (Spring ERA-)
Linear (NECBL ERA-)
SUMMARY OUTPUT
R Square 0.2611
Observations 23
ANOVA
Regression 1 11228.11344 11228.11344 7.421118292 0.012709
Residual 21 31772.89094 1512.994807
Total 22 43001.00437
Intercept 42.323 20.5609406 2.058406215 0.052172693 -0.43605 85.08158 -0.43605 85.08158
Spring ERA- 0.5192 0.190580852 2.724172956 0.012709 0.122841 0.91551 0.122841 0.91551
Ivy League ERA- Expected NECBL ERA-
50 68.28
60 73.47
70 78.67
80 83.86
90 89.05
100 94.24
110 99.43
120 104.62
130 109.82
140 115.01
150 120.20

56 | P a g e
MAAC Analysis– 95% confidence for ERA-and 90% confidence forFIP-
0
50
100
150
200
250
0 10 20 30 40
ERA-
# of Players
MAAC ERA-
Spring ERA-
NECBL ERA-
Linear (NECBL ERA-)
SUMMARY OUTPUT
R Square 0.11049
Observations 37
ANOVA
Regression 1 7221.46299 7221.463 4.3476398 0.04442
Residual 35 58135.2679 1661.008
Total 36 65356.7309
Intercept 55.2997 21.1010877 2.620704 0.01289429 12.46223 98.1372 12.46223 98.1372
Spring ERA-0.45711 0.21922571 2.085099 0.04442 0.012056 0.902159 0.012056 0.902159
MAAC ERA- Expected NECBL ERA-
50 78.16
60 82.73
70 87.30
80 91.87
90 96.44
100 101.01
110 105.58
120 110.15
130 114.72
140 119.29
150 123.87

57 | P a g e
0
50
100
150
200
250
0 10 20 30 40
FIP-
# of Players
MAAC FIP-
Spring FIP-
NECBL FIP-
Linear (Spring
FIP-)
Linear (NECBL
FIP-)
SUMMARY OUTPUT
R Square 0.0995
Observations 37
ANOVA
Regression 1 4603.012422 4603.012422 3.866817 0.05721982
Residual 35 41663.57429 1190.387837
Total 36 46266.58671
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0%Upper 95.0%
Intercept 16.286 38.29833909 0.425238848 0.673266 -61.4638197 94.0357029 -61.4638 94.0357029
FIP- 0.7727 0.392931516 1.966422492 0.057 -0.02502401 1.57036275 -0.02502 1.57036275
MAAC FIP- Expected NECBL FIP-
50 54.92
60 62.65
70 70.37
80 78.10
90 85.83
100 93.55
110 101.28
120 109.01
130 116.73
140 124.46
150 132.19

58 | P a g e
Pac 12 Analysis – 95% confidence for FIP- (Minus 2 outliers)
The data was insignificant when including two extremely high FIP- (values of 230 in 2011 &
2012 in the Pac 12). When eliminating those outliers – the data becomes significant.
0
50
100
150
200
250
0 5 10 15 20 25
FIP-
# of Players
Pac 12 FIP-
Spring FIP-
NECBL FIP-
Linear (NECBL FIP-)
SUMMARY OUTPUT
R Square 0.18812
Observations 21
ANOVA
Regression 1 1666.082 1666.082 4.40236607 0.049492
Residual 19 7190.577 378.4514
Total 20 8856.658
Intercept 45.5791 20.46083 2.227626 0.03818847 2.754065 88.40407 2.754065 88.40407
230.0356 0.34211 0.163052 2.098182 0.0495 0.000841 0.683383 0.000841 0.683383
Pac 12 FIP- Expected NECBL FIP-
50 62.68
60 66.11
70 69.53
80 72.95
90 76.37
100 79.79
110 83.21
120 86.63
130 90.05
140 93.47
150 96.90

59 | P a g e
Sun Belt Analysis – 95% confidence for ERA-
0
50
100
150
200
250
0 2 4 6 8 10
ERA-
# of Players
Sun Belt ERA-
Spring ERA-
NECBL ERA-
Linear (NECBL ERA-)
SUMMARY OUTPUT
R Square 0.68096
Observations 8
ANOVA
Regression 1 8628.465724 8628.465724 12.80665195 0.011662
Residual 6 4042.492491 673.7487486
Total 7 12670.95822
Intercept 3.1615 19.04261991 0.166024133 0.873591185 -43.4341 49.75715 -43.4341 49.75715
Spring ERA- 0.6502 0.181690132 3.578638281 0.01166 0.205624 1.094783 0.205624 1.094783
Sun Belt ERA- Expected NECBL ERA-
50 35.67
60 42.17
70 48.68
80 55.18
90 61.68
100 68.18
110 74.68
120 81.19
130 87.69
140 94.19
150 100.69

60 | P a g e
MiscellaneousFindings
I wanted to see if there were any conferences where lefties or righties consistently performed
better than the other.
There were two notable results.
Below is a table of all lefty hitters from the ACC and their performance in the NECBL.
Over 70%of lefty hitters from the ACC performed above league average in the NECBL in
terms of wRC+ and 67%performed above league average in the NECBL in terms of OPS+
The average wRC+ for lefty hitters from the ACC in the NECBL is 111
The average OPS+ for lefty hitters from the ACC in the NECBL is 120
Year Name B/T wRC+ OPS+
2010 Blow, M L/R 128 162
2010 Gianis, J L/R 119 130
2011 Kronenfeld, P L/L 118 108
2011 Mack, C L/L 113 98
2011 Podlas, M L/L 47 52
2011 Kiene, T L/R 142 162
2011 Horan, T L/R 156 173
2012 Spingola, D L/L 91 110
2012 White, C L/L 144 159
2012 Zengel, T L/L 85 98
2012 Keniry, C L/R 103 106
2012 Pare, M L/R 113 167
2012 Santos, J L/R 79 60
2012 Papi, M L/R 108 105
2012 Kronenfeld, P L/R 102 102
2013 Spingola, D L/L 115 127
2013 Shaw, C L/R 159 198
2013 Kennedy, G L/R 95 84
2013 Papio, A L/R 111 131
2013 Triller, M L/R 83 68
2014 Delph, T L/L 102 84
2014 Zunica, B L/R 120 171
2014 Tiberi, B L/R 155 189
2014 Lyman, C L/R 138 182
2014 Papio, A L/R 125 126
2014 Biggio, C L/R 76 54
2015 Jackson, R L/R 57 43

61 | P a g e
Below is a table of all lefty hitters from the MAACand their performance in the NECBL
Over 90%of lefty hitters from the MAAC have performed at league average or better in the
NECBL in terms of wRC+
Over 76%of lefty hitters from the MAAC have performed at league average or better in the
NECBL in terms of OPS+
The average wRC+ for lefty hitters from the MAAC in the NECBL is 116
The average OPS+ for lefty hitters from the MAAC in the NECBL is 116
Year Name B/T wRC+ OPS+
2010 Nathans, T L/R 127 128
2010 McCann, M L/R 144 143
2010 Nugent, B L/R 105 86
2010 Quaranto, K L/R 124 146
2011 Coppinger, R L/R 108 102
2012 Orefice, M L/L 110 122
2012 Klock, J L/L 62 60
2012 Salvo, S L/R 102 77
2013 Guglietti, V L/L 123 120
2014 Guglietti, V L/L 142 173
2014 Byrne, M L/R 84 118
2014 Pagano, M L/R 118 100
2014 Wilgus, S L/R 122 108
2015 Lumley, J L/R 131 129
2015 Brucker, J L/R 105 85
2015 Laberton, G L/R 100 63
2015 Shea, D L/R 109 120
2015 Hughes, S L/R 141 150
2015 Gaetano, C L/R 127 121
2015 Iannotti, L L/R 140 141
2015 Pescitelli, R L/R 106 138

62 | P a g e
Year Name School Conference Spring K% NECBL wRC+
2010 Onorati, M Manhattan MAAC 4% 93
2010 Gomez, A Vanderbilt SEC 4% 145
2011 Barrett, B University of Southern Maine Little East 4% 62
2013 Patterson, S University of Cal Davis Big West 5% 123
2012 Barrett, B University of Southern Maine Little East 5% 86
2015 Dexter, S University of Southern Maine Little East 5% 136
2015 Luopa, L Eckerd College Sunshine State 5% 100
2011 Cammans, J University of Rhode Island Atlantic 10 6% 124
2012 Shank, Z Marist College MAAC 6% 108
2015 Hardardt, C Hofstra CAA 6% 76
2012 Diekroeger, D Stanford Pac-12 6% 130
2013 Sportman, J Central Connecticut State Northeast 6% 139
2013 Donley, S Indiana Big Ten 7% 131
2011 Ciocchi, D Binghamton America East 7% 111
2015 Dejesus, M Ohio University MAC 7% 123
2014 Xepoleas, R George Washington Atlantic 10 7% 113
2013 English, A Barry University Sunshine State 7% 133
2014 Charbonneau, B LeMoyne College Northeast 10 7% 80
2013 Keller, A Princeton Ivy 7% 136
2012 Patron, I Long Beach State Big West 8% 160
2011 Conley, T UMASS Amherst Atlantic 10 8% 48
2015 Schanz, D Binghamton America East 8% 122
2010 Ciocchi, D Binghamton America East 8% 130
2013 Young, A University of Cal Davis Big West 8% 85
2014 Dexter, S University of Southern Maine Little East 8% 120
2014 Rinn, R Bryant Northeast 8% 120
2013 Wiese, P LeMoyne College Northeast 10 8% 130
2014 Coman, R University of Virginia ACC 8% 130
2015 Parenty, J Stony Brook America East 9% 127
2012 English, A Barry University Sunshine State 9% 117
2014 Balzano, S University of Maine America East 9% 86
2015 Mascelli, N Wagner College Northeast 9% 119
2014 Diamond, A Belmont Ohio Valley 9% 91
2015 Gazzola, A Stony Brook University America East 9% 122
2014 Roulis, T Dartmouth Ivy 9% 118
2010 Doane, K East Tennessee State Atlantic Sun 9% 98
2012 Black, T University of Maine America East 10% 95
2015 Knightes, R St Johns Big East 10% 100
2014 Siena, V UCONN AAC 10% 111
2010 Cantwell, P SUNY Stony Brook America East 10% 140
2012 Peragine, C SUNY Stony Brook America East 10% 138
2012 Sportman, J Central Connecticut State Northeast 10% 109
2013 Torres, J Iona College MAAC 10% 142
2014 Bunn, J VCU Atlantic 10 10% 147
2012 White, C Maryland ACC 10% 144
2013 Bailey, C Georgia State CAA 10% 133
2012 Collins, D Troy Sun Belt 10% 159
2012 Lindemuth, R College of William & Mary CAA 10% 129
2013 Anderson, C Bryant Northeast 10% 136
2010 Fontaine, T UMASS Boston Little East 10% -15
2011 Brown, K Bryant Northeast 10% 100
Looking at a hitter’s K/BB ratio can be a good indicator of plate discipline and a solid statistic to
use when evaluating a player. Below is a chart of every single player thathas played in the
NECBL whose K% was 10% or lower in their spring season prior to their NECBL appearance.
You can see that evaluating how much a player strikes out in the spring is a good indicator of
how the player will perform in the NECBL.
The average wRC+ in the
NECBL for players with
K%’s ≤ 10% in the spring
is 115
Over 76%of players
with a K% ≤ 10% in their
spring seasons perform at
league average or better in
the NECBL

63 | P a g e
Year Name School Conference Spring K% Spring BB% NECBL wRC+
2010 Quaranto, K Siena Collge MAAC 14% 16% 124
2010 Ciocchi, D Binghamton America East 8% 11% 130
2010 Gomez, A Vanderbilt SEC 4% 7% 145
2010 Onorati, M Manhattan MAAC 4% 5% 93
2011 Gregor, C Vanderbilt SEC 18% 19% 165
2011 Freeman, R Kennesaw State Atlantic Sun 14% 17% 116
2011 Ciocchi, D Binghamton American East 7% 17% 111
2011 Conley, T UMASS Amherst Atlantic 10 8% 12% 48
2011 Cammans, J University of Rhode Island Atlantic 10 6% 8% 124
2012 Kelly, R St. Anslem College Northeast 10 14% 23% 154
2012 Boulter, M Southern New Hampshire Northeast 10 17% 23% 71
2012 Planas-Arteaga, S Barry University Sunshine State 20% 22% 133
2012 Pierce, L Troy Sun Belt 12% 19% 145
2012 Butera, B Boston College ACC 13% 16% 78
2012 Orefice, M Marist College MAAC 12% 16% 110
2012 Patron, I Long Beach State Big West 8% 15% 160
2012 Torres, J Iona College MAAC 12% 14% 83
2012 White, C Maryland ACC 10% 14% 144
2012 Keur, J Michigan State Big Ten 11% 12% 120
2012 English, A Barry University Sunshine State 9% 10% 117
2012 Diekroeger, D Stanford Pac-12 6% 9% 130
2013 Razzino, J Franklin Pierce Northeast 10 21% 35% 74
2013 Ferreira, E Harvard Ivy 22% 26% 106
2013 Kennedy, G University of Miami ACC 19% 22% 95
2013 Spingola, D Georgia Tech ACC 17% 18% 115
2013 Plourde, R Fairfield MAAC 13% 18% 144
2013 Stubbs, G USC Pac-12 11% 15% 134
2013 Ford, M Hofstra U CAA 12% 15% 95
2013 Richardson, R Michigan State Big Ten 11% 13% 107
2013 Blanden, Z Binghamton America East 11% 12% 114
2013 Wiese, P LeMoyne College Northeast 10 8% 12% 130
2013 Keller, A Princeton Ivy 7% 10% 136
2013 English, A Barry University Sunshine State 7% 9% 133
2013 Patterson, S University of Cal Davis Big West 5% 9% 123
2013 Donley, S Indiana Big Ten 7% 9% 131
2014 Valdez, R Barry University Sunshine State 17% 25% 119
2014 Caruso, A St. John's Big East 14% 23% 117
2014 Delph, T Florida State ACC 16% 18% 102
2014 Berman, S Santa Clara WCC 11% 16% 119
2014 Lynch, T Southern Mississippi Conference USA 13% 16% 162
2014 Rinn, R Bryant Northeast 8% 16% 120
2014 Crinella, F Merrimack College Northeast 10 15% 15% 109
2014 Machin, V VCU Atlantic 10 11% 15% 87
2014 Weigel, Z Seton Hall Big East 13% 15% 108
2014 Wright, C Kansas Big 12 12% 15% 86
2014 Parenty, J SUNY Stony Brook America East 13% 14% 93
2014 McGrath, P Washington State Pac-12 11% 12% 60
2014 Coman, R University of Virginia ACC 8% 12% 130
2014 Diamond, A Belmont Ohio Valley 9% 12% 91
2014 Balzano, S University of Maine America East 9% 10% 86
2014 Charbonneau, B LeMoyne College Northeast 10 7% 9% 80
2014 Xepoleas, R George Washington Atlantic 10 7% 7% 113
2015 Palomaki, J Boston College ACC 14% 28% 88
2015 Copeland, G Austin Peay State Ohio Valley 16% 18% 117
2015 Grote, C Furman Southern 15% 18% 106
2015 Boyher, L Columbia Ivy 15% 17% 91
2015 Mascelli, N Wagner College Northeast 9% 15% 119
2015 Dejesus, M Ohio University MAC 7% 15% 123
2015 Dawson, N University of Southern Mississippi Conference USA 12% 14% 103
2015 Lashley, B Florida Atlantic University Conference USA 12% 13% 119
2015 Parenty, J Stony Brook America East 9% 12% 127
2015 Gazzola, A Stony Brook University America East 9% 11% 122
2015 Dexter, S University of Southern Maine Little East 5% 9% 136
2015 Hardardt, C Hofstra CAA 6% 8% 76
2015 Luopa, L Eckerd College Sunshine State 5% 7% 100
Looking for players
with a K% ≤ 10% isn’t
the only thing an
NECBL general
manager should be
doing. Here is a chart
with every single
player that has played
in the NECBL whose
BB% was greater than
their K% in their
spring seasons prior to
their NECBL
appearance
The average wRC+ in
the NECBL for players
whose BB% was greater
than their K% in their
respective spring
seasons is 113
Over 72%of players
whose BB% was greater
than their K% in their
spring seasons
performed at league
average or better in the
NECBL

64 | P a g e
Year Name School Conference Spring SB/PA% NECBL wRC+
2015 Nixon, C Kennessaw State Atlantic Sun 14.29% 101
2014 Crinella, F Merrimack College Northeast 10 14.14% 109
2015 Jenkins, D Seton Hall Big East 14.13% 96
2013 Wiese, P LeMoyne College Northeast 10 13.64% 130
2014 LaVorgna, C Franklin Pierce Northeast 10 13.04% 71
2014 Krische, M Canisius College MAAC 12.95% 111
2013 Torres, J Iona College MAAC 12.38% 142
2011 Johnson, K Washington State Pac-12 12.33% 111
2014 Ocello, E Holy Cross College Patriot 12.06% 143
2015 Sundberg, J UCONN AAC 11.22% 118
2013 Witkus, A Fairfield MAAC 10.60% 115
2014 Sundberg, J UCONN AAC 10.47% 92
2014 Handley, T SUNY Stony Brook America East 10.26% 152
2014 Martin, M Harvard Ivy 10.24% 149
2012 Black, T University of Maine America East 9.94% 120
2013 Carcone, J College of St. Rose Northeast 10 9.93% 108
2011 LeBel, M University of Rhode Island Atlantic 10 9.92% 166
2011 Burke, C Iona College MAAC 9.87% 117
2011 Cammans, J University of Rhode Island Atlantic 10 9.82% 124
2010 Coulombe, T University of Rhode Island Atlantic 10 9.39% 104
2015 Tufts, R Virginia Tech ACC 9.35% 96
2015 Dixon, T Samford Southern 9.13% 104
2013 Balzano, S University of Maine America East 8.90% 138
2013 Pezzuto, G Southern New Hampshire Northeast 10 8.89% 78
2012 Witkus, A Fairfield University MAAC 8.82% 100
2014 Biggio, C Notre Dame ACC 8.33% 76
2013 Anderson, C Bryant Northeast 8.26% 136
2015 Parenty, J Stony Brook America East 7.88% 127
2012 Roy, J University of Rhode Island Atlantic 10 7.86% 138
2013 Ford, M Hofstra U CAA 7.75% 95
2015 McCain, G Oklahoma State Big 12 7.61% 114
2015 Copeland, G Austin Peay State Ohio Valley 7.50% 117
2012 Coffman, K Arizona State Pac-12 7.33% 100
2010 Stafford, R Marshall Conference USA 7.26% 73
2012 Obrien, B Southern New Hampshire Northeast 10 7.26% 91
2010 Lebel, M University of Rhode Island Atlantic 10 7.14% 139
2013 Santomauro, A Lafayette College Patriot 7.08% 108
2013 Plourde, R Fairfield MAAC 7.01% 144
Next I wanted to see if speed translated to offensive success in the NECBL. Instead of just
looking at total stolen bases, I looked at Stolen Bases per Plate Appearance, since some players
had more opportunities than others. This gives us the percentage of plate appearances in which
the hitter eventually stole a base. This seems to be a important thing to look at when deciding
which players to acquire in the NECBL. Below is a table of all players who played in the
NECBL with SB/PA % > 7%. This seemed to be the cutoff as the average wRC+ and percentage
of players above league average fell when trying to include players > than 6%.
The average wRC+ in
the NECBL for
players with a
SB/PA% > 7% in their
spring seasons is
115
Over 76%of
players with a
SB/PA% > 7%
performed at league
average or better in
the NECBL.

65 | P a g e
Summary
There are many conclusions to draw from all of the data analyzed throughout this report. Some
of the main ones that stand out to me are as follows
 Player’s offensive ability definitely increases as they get older
 NJCAA players are historically poor performers in the NECBL
o 87 wRC+
o 109 ERA-
 Conferences that produce the best offensive players (with a reliable amount of data)
o Ivy League
o America East
o MAAC
o Big Ten
 Best Offensive PIR
o ACC
o Conference USA
o SEC
o Pac 12
 Players from these conferences are likely to improve from spring to
summer
 Conferences that produce the best pitchers (with a reliable amount of data)
o Northeast
o Ivy League
o MAAC
o ACC
o Atlantic 10
o SEC
 Best Pitching PIR
o Atlantic Sun
o Pac 12
 Players from these conferences are likely to improve from spring to
summer

66 | P a g e
 NECBL general managers should basically always take the following players on their
rosters (all offensive stats require a minimum of 50 at bats) (all pitching stats require a
minimum of 15 IP or 10 appearances)
o All lefty hitters from the ACC
o All lefty hitters from the MAAC
o Hitters with a K% ≤ 10%
o Hitters whose BB% > K%
o Hitters whose SB/PA% > 7%
o America East hitters with a wRC+ ≥ 100
o Northeast 10 hitters with a wRC+ ≥ 130
o Patriot League hitters with a wRC+ ≥ 100
o ACC hitters with a wRC+ ≥ 90
o Atlantic 10 hitters with a wRC+ ≥ 120
o Big Ten hitters with a wRC+ ≥ 90
o Ivy League hitters with a wRC+ ≥ 90
o Ohio Valley hitters with a wRC+ ≥ 120
o Atlantic Sun hitters with an OPS+≥ 100
o MAC hitters with an OPS+ ≥ 150
o Sunshine State hitters with an OPS+ ≥ 100
o ALL pitchers from the following conferences
 Atlantic Sun
 Pac 12
 Sun Belt
o Big East pitchers with a FIP- ≤ 140
o Big Ten pitchers with a FIP- ≤ 110
o Ivy League pitchers with an ERA- ≤ 110
o MAAC pitchers with an ERA- ≤ 100

67 | P a g e
Year Name School wOBA pf_wRAA wRC wRC+ BABIP OPS OPS+ K% BB%
2015 Agresti, J Binghamton 0.333 5.88 17 93 0.267 0.704 110 22% 15%
2015 Klages, J University of Missouri 0.282 1.00 11 64 0.222 0.526 57 33% 23%
2015 Sundberg, J UCONN 0.374 7.12 15 118 0.409 0.817 144 24% 19%
2015 Burger, Z Louisiana Tech University 0.377 17.65 37 120 0.270 0.730 118 9% 16%
2015 Cox, B Mercyhurst College 0.306 4.37 19 78 0.281 0.553 65 16% 6%
2015 Bergami, D Springfield College 0.280 0.92 13 62 0.225 0.467 40 12% 8%
2015 Michelangeli, E 0.348 5.10 13 103 0.400 0.828 148 24% 19%
2015 Maldonado, F Pittsburgh 0.338 8.15 22 96 0.333 0.687 105 15% 5%
2015 Acker, C VCU 0.397 16.16 31 131 0.413 0.884 164 9% 6%
2015 Triano, C SUNY Purchase 0.328 3.45 10 90 0.273 0.743 122 32% 8%
2015 Bunn, J 0.317 3.40 12 84 0.304 0.641 92 15% 8%
2014 Lavy, Z University of Missouri 0.339 7.82 28 85 0.317 0.676 89 13% 7%
2014 Morgan, B Kennesaw State 0.315 2.44 15 71 0.284 0.591 67 15% 7%
2014 Ryan, A Dayton 0.342 8.49 29 87 0.280 0.658 84 13% 5%
2014 Gutierrez, H University of Michigan 0.288 -0.21 13 55 0.313 0.577 63 32% 9%
2014 DelDebbio, C Hartford 0.332 3.41 14 81 0.288 0.643 80 14% 4%
2014 Huesman, A Dayton 0.362 5.29 15 99 0.325 0.799 120 25% 26%
2014 Pearson, L University of Missouri 0.267 -1.92 8 42 0.302 0.547 55 29% 8%
2014 Ring, J University of Missouri 0.292 0.32 20 57 0.351 0.624 75 28% 10%
2014 Palacios, J 0.335 3.35 13 83 0.293 0.686 91 13% 10%
2014 Machin, V VCU 0.342 9.24 32 87 0.284 0.706 96 21% 13%
2014 Poduslenko, J Seton Hall 0.305 1.57 15 65 0.262 0.603 70 27% 16%
2013 Palmer, R Southern New Hampshire U 0.340 8.02 27 87 0.307 0.740 105 27% 7%
2013 Caputo, J SUNY Stony Brook 0.288 0.24 17 56 0.337 0.588 66 24% 7%
2013 David, C UCONN 0.206 -4.39 3 8 0.231 0.348 4 28% 2%
2013 Vanaman, C Tulane 0.242 -2.76 5 29 0.179 0.390 15 29% 7%
2013 Tuccio, A Siena 0.304 2.68 22 66 0.378 0.693 92 33% 13%
2013 Spingola, D Georgia Tech 0.388 17.46 39 115 0.380 0.826 127 17% 8%
2013 Blanden, Z Binghamton 0.386 17.13 39 114 0.336 0.785 116 21% 19%
2013 Testani, J UCONN 0.308 2.50 17 68 0.288 0.647 81 41% 13%
2013 Yavarone, E UCONN 0.312 1.83 11 70 0.280 0.619 73 18% 10%
2013 Pezzuto, G Southern New Hampshire U 0.325 3.86 17 78 0.268 0.593 67 17% 10%
2013 Cruz, A Georgia Tech 0.339 6.64 23 86 0.397 0.773 113 29% 8%
2013 Riopedre, C 0.306 1.54 12 67 0.340 0.670 87 24% 13%
Danbury Westerners Hitters, 2013-2015

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2012 Orefice, M Marist College 0.418 18.52 51 110 0.362 0.965 122 18% 19%
2012 Boutler, C Southern New Hampshire 0.336 1.41 14 71 0.238 0.686 64 25% 25%
2012 Thomas, M University of Kentucky 0.223 -6.40 6 17 0.250 0.435 11 38% 10%
2012 Ivory, J University of Missouri 0.283 -3.02 13 45 0.390 0.660 57 29% 2%
2012 Shank, Z Marist College 0.415 17.29 49 108 0.328 0.850 97 11% 9%
2012 Boulter, M Southern New Hampshire 0.390 11.40 39 97 0.365 0.821 94 16% 22%
2012 Spingola, D Georgia Tech 0.378 8.68 34 91 0.396 0.919 110 25% 6%
2012 Zengel, T UNC Chapel Hill 0.366 7.56 35 85 0.294 0.863 98 25% 13%
2012 Wernicki, K Virginia Tech 0.251 -4.41 7 30 0.282 0.602 44 32% 7%
2012 Hagan, S Binghamton 0.343 1.92 15 74 0.257 0.876 100 36% 13%
2012 Garner, A Tulane 0.365 7.33 35 84 0.260 0.807 85 23% 3%
2012 Ake, J UNC Chapel Hill 0.325 0.61 14 65 0.340 0.689 64 25% 12%
2012 Gronsky, J Monmouth 0.347 3.61 25 76 0.326 0.775 80 22% 3%
2011 Ciocchi, D Binghamton Univeristy 0.373 7.57 20 111 0.366 0.818 116 13% 8%
2011 Morgan, C Virginia Tech 0.340 6.29 24 90 0.398 0.767 103 23% 6%
2011 Swingle, S Franklin Pierce 0.277 -1.13 9 52 0.350 0.643 74 37% 15%
2011 Richardson, K St Johns University 0.352 5.41 18 98 0.300 0.681 83 22% 9%
2011 Butler, C Georgia Tech 0.308 1.98 19 71 0.378 0.700 88 31% 14%
2011 Everett, D University of Missouri 0.331 4.64 21 85 0.306 0.680 82 19% 8%
2011 Horan, T Virginia Tech 0.446 21.90 41 156 0.351 1.053 173 19% 5%
2011 Krietemeier, T University of Nebraska Lincoln 0.371 12.69 34 110 0.392 0.838 121 18% 6%
2011 Garner, A Tulane 0.430 17.01 34 146 0.463 1.047 172 21% 5%
2011 Opel, D University of Missouri 0.377 11.18 29 113 0.373 0.910 139 27% 20%
2011 Ford, M Princeton 0.336 4.72 19 88 0.338 0.683 83 18% 11%
2011 Convissar, K Maryland 0.416 9.31 19 138 0.396 0.980 157 17% 8%
2011 Waylock, C Iowa Western CC 0.353 7.91 25 99 0.333 0.716 92 14% 12%
2010 Meeks, T Marshall 0.405 12.42 26 134 0.327 0.975 165 27% 25%
2010 Williams, M University of Kentucky 0.282 -0.90 18 57 0.318 0.641 79 31% 6%
2010 Rodriguez, A Maryland 0.321 3.87 19 81 0.271 0.610 71 13% 4%
2010 Ciocchi, D Binghamton 0.398 17.58 39 130 0.315 0.771 112 9% 14%
2010 Kownacki, B Fordham U 0.284 -0.45 12 58 0.306 0.587 65 38% 13%
2010 Barry, B Tulane 0.310 1.66 12 74 0.277 0.597 68 27% 15%
2010 Hajjar, A Fairfield U 0.306 1.15 10 72 0.239 0.549 55 22% 2%
2010 Nathans, T Fairfield U 0.393 18.35 41 127 0.294 0.833 128 15% 11%
2010 Gianis, J North Carolina State 0.381 5.34 13 119 0.396 0.840 130 9% 6%
2010 Knief, B UNC Chapel Hill 0.315 2.40 14 78 0.319 0.621 74 18% 6%
2010 Brennan, J St. John's University 0.317 2.63 14 79 0.415 0.728 101 33% 15%
2010 Stafford, R Marshall 0.308 2.07 16 73 0.263 0.655 83 36% 10%
2010 Boudreaux, B Tulane 0.376 9.88 25 116 0.303 0.757 109 14% 19%
2010 Ingui, D Franklin Pierce 0.357 11.36 33 104 0.347 0.749 107 16% 10%
Danbury Westerners Hitters, 2010-2012

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Year Name School IP BAA ERA ERA- FIP FIP- K% BB%
2015 Morris, C Seton Hall 38.67 0.268 5.35 148 4.59 127 15% 12%
2015 Rivera, S Vanguard University 28.67 0.257 3.45 96 5.09 141 16% 11%
2015 Lyman, D Lee University 11.33 0.25 5.56 154 6.86 190 16% 19%
2015 O'Neill, P Eastern Connecticut State 23.00 0.259 3.13 87 3.59 99 15% 8%
2015 Tinkham, S Grinnell College 48.67 0.267 3.33 92 3.70 103 12% 6%
2015 Leeds, M Lafeyette College 38.33 0.283 4.93 136 3.04 84 12% 7%
2015 Baker, B University of Missouri 33 0.281 4.91 136 4.89 135 15% 12%
2015 Dabney, L University of Missouri 17.67 0.203 7.64 211 4.76 132 14% 20%
2015 Ledesma, J Lackawanna College 35 0.241 6.17 171 7.15 198 12% 18%
2014 Murphy, B Hartford 33.67 0.252 3.47 85 3.83 94 19% 9%
2014 Burum, S Seton Hall 25.33 0.31 3.91 96 1.98 49 21% 7%
2014 Arena, J LIU Post 20.67 0.348 6.10 150 3.30 81 12% 7%
2014 Marks, R Columbia 21.67 0.195 3.74 92 4.47 110 17% 10%
2014 Santiago, E Western Connecticut State 20.67 0.333 7.40 182 4.65 114 14% 12%
2014 Farina, A Lafayette College 33 0.225 3.27 80 2.95 72 19% 8%
2014 Schwaab, A University of Missouri 34.33 0.235 2.10 52 3.76 92 17% 7%
2014 Mintz, Levi Mississippi State 14.33 0.264 4.40 108 3.60 89 15% 15%
2014 Holmes, T San Jacinto College North 27 0.288 4.00 98 1.28 32 27% 7%
2014 Torres, K Western Oklahoma State 22 0.238 4.50 111 4.58 113 18% 11%
2013 Pashuck, J Maryland 27.67 0.242 1.63 39 2.55 61 24% 6%
2013 Green, S Boston College 39.67 0.291 3.40 81 3.70 88 14% 9%
2013 Carter, R Hartford 22 0.307 3.68 88 3.60 86 19% 11%
2013 Lejeune, C George Washington 20 0.291 4.50 107 2.45 58 30% 4%
2013 Murphy, J Fordham 36.33 0.289 5.45 130 3.47 83 17% 8%
2013 Corsi, R Rutgers 22.67 0.261 3.57 85 3.90 93 17% 12%
2013 Tax, Z Columbia 35.67 0.205 1.26 30 2.80 67 13% 5%
2013 Fryer, N Siena 19.33 0.235 3.72 89 3.76 90 34% 11%
2013 Blanc, R Franklin Pierce 33.33 0.276 2.97 71 3.86 92 15% 6%
2013 Ascher, S SUNY Oneonta 44 0.278 3.89 93 3.47 83 19% 5%
2013 Bonilla B Grand Canyon U 19.33 0.224 8.38 200 3.14 75 31% 14%
Danbury WesternersPitchers, 2013-2015

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Year Name School IP BAA ERA ERA- FIP FIP- K% BB%
2012 Stinnett, J Maryland 17.33 0.109 1.56 25 4.37 70 29% 9%
2012 Adkins, B Miami 20.33 0.134 1.77 28 4.53 72 23% 9%
2012 Brewster, B Maryland 27.33 0.260 3.62 58 4.48 71 20% 10%
2012 Sterman, I Virginia Tech 13 0.260 9.69 154 5.94 95 27% 17%
2012 Porter, J Fordham University 23 0.302 7.04 112 5.70 91 12% 5%
2012 Catalina, S UCONN 37 0.280 3.16 50 4.33 69 17% 7%
2012 Tax, Z Columbia 20.67 0.269 3.48 56 5.34 85 18% 14%
2012 Houseal, B Marist 63.67 0.230 3.25 52 5.99 95 15% 6%
2012 Gibson, D Southern New Hampshire 26 0.226 5.54 88 5.56 89 20% 12%
2012 Breidenbach, F West Chester U 30.33 0.323 7.42 118 6.36 101 16% 10%
2012 Luksis, E University of Tampa 39.67 0.323 5.90 94 8.02 128 13% 8%
2011 Augliera, M Binghamton University 34.67 0.25 3.12 70 3.60 81 20% 6%
2011 DeCecco, S South Carolina Upstate 32.33 0.294 3.62 81 3.79 85 19% 12%
2011 Eagleson, S Johns Hopkins 42.67 0.286 4.01 90 4.03 91 11% 5%
2011 Link, K Princeton 44.67 0.235 2.42 54 2.62 59 16% 3%
2011 Ford, M Princeton 46.67 0.313 4.24 95 3.05 69 20% 9%
2011 Luksis, E Manhattan College 28.67 0.244 7.22 162 2.56 58 19% 5%
2011 Williams, N University of Tennessee 37.33 0.227 4.34 98 3.08 69 28% 13%
2011 Wijas, W University of Kentucky 15.67 0.288 4.59 103 2.41 54 27% 7%
2011 Reed, J 25.33 0.229 1.78 40 1.59 36 39% 5%
2010 Brechbuehler, T* UNC Chapel Hill 19.67 0.267 8.69 210 6.61 160 15% 18%
2010 Hauschild, M Dayton 58.00 0.176 1.24 30 2.33 56 22% 7%
2010 Lahram, B Dayton 35.67 0.259 4.79 116 3.95 96 18% 13%
2010 Gardek, I* Dayton 18.67 0.182 3.37 82 3.00 73 25% 14%
2010 Hagan, S St. John's University 37.67 0.275 5.50 133 4.01 97 17% 9%
2010 Catalina, S* UCONN 24.33 0.295 5.92 143 4.29 104 13% 13%
2010 Anderson, M* East Carolina U 19.00 0.222 5.68 138 3.79 92 24% 9%
2010 Anarumo, M* LeMoyne College 22.00 0.309 3.27 79 3.01 73 19% 12%
2010 Clark, D Elon U 36.00 0.246 2.25 54 3.05 74 19% 3%
Danbury WesternersPitchers, 2010-2012

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2015 Foreman, C University of Rhode Island 0.371 10.89 23 119 0.268 0.762 128 16% 21%
2015 Luopa, L Eckerd College 0.338 11.76 32 100 0.289 0.624 87 9% 10%
2015 Matheny, S Washington State 0.300 2.91 15 77 0.295 0.562 68 23% 18%
2015 Hardardt, C Hofstra 0.297 2.91 16 76 0.222 0.468 40 11% 8%
2015 Fitzsimons, C Central Connecticut State 0.248 -3.24 13 47 0.300 0.552 65 35% 5%
2015 Christman, G Butler 0.308 6.95 28 82 0.320 0.652 95 23% 12%
2015 Annunziata, M 0.251 -1.62 7 48 0.230 0.423 26 21% 3%
2015 Crisler, L Indiana University 0.287 1.80 15 70 0.314 0.609 82 30% 4%
2015 Karl, R Corenll University 0.333 9.53 27 97 0.271 0.752 124 30% 10%
2015 Brocato, A 0.361 6.24 14 114 0.271 0.790 136 21% 5%
2015 Geannelis, M UMASS Amherst 0.288 1.68 13 70 0.282 0.528 58 22% 11%
2015 Celucci, D Bryant 0.282 1.55 17 67 0.302 0.538 61 27% 12%
2014 McCarty, A Vanderbilt 0.304 1.86 14 70 0.311 0.602 73 21% 5%
2014 McGrath, P Washington State 0.287 0.15 13 60 0.247 0.496 44 18% 4%
2014 Summers, R Louisville 0.319 4.50 21 80 0.293 0.670 91 28% 7%
2014 Coman, R University of Virginia 0.402 10.60 22 130 0.338 0.830 134 8% 7%
2014 Fitzsimons, C Central Connecticut State 0.309 2.43 15 73 0.323 0.662 89 29% 6%
2014 Sheetz, B Hartford 0.345 7.48 23 95 0.322 0.753 113 18% 6%
2014 Liquori, A Kennesaw State 0.324 4.34 18 82 0.358 0.713 103 30% 15%
2014 Smith, S Texas Tech 0.303 1.25 10 70 0.317 0.683 94 32% 10%
2014 Lauricella, Z St. John's 0.360 11.29 30 104 0.313 0.812 129 23% 14%
2014 Simonetti, C 0.261 -1.51 6 44 0.308 0.644 84 45% 9%
2014 Hall, D Cochise College 0.269 -1.10 7 49 0.186 0.414 22 20% 9%
2014 Dennis, B St. John's 0.318 5.20 25 78 0.336 0.640 83 25% 11%
2014 Mederos, J St. John's 0.404 14.54 29 131 0.348 0.853 140 12% 11%
2014 Perez, T St. Leo College 0.275 -1.33 14 53 0.274 0.520 51 25% 7%
2013 Gutierrez, E Texas Tech 0.353 11.30 31 100 0.259 0.662 88 14% 13%
2013 Monnot, T Kent State 0.344 6.90 21 95 0.324 0.741 110 21% 8%
2013 Swingle, S Grand Canyon U 0.361 4.80 12 105 0.429 0.884 148 37% 12%
2013 Lauricella, Z St John's U 0.350 10.16 29 98 0.280 0.726 106 20% 9%
2013 Sportman, J Central Connecticut State 0.419 23.42 45 139 0.377 0.933 162 12% 13%
2013 Lucas, Z Louisville 0.368 11.82 29 109 0.427 0.847 138 27% 4%
2013 Ford, M Hofstra U 0.343 10.10 30 95 0.310 0.650 85 10% 7%
2013 Lukach, R Hartford 0.319 3.72 16 80 0.411 0.772 118 35% 4%
2013 Ogrady, B Rutgers 0.409 17.35 34 133 0.322 0.904 154 18% 14%
2013 Nichols, D University of Georgia 0.340 7.92 25 93 0.298 0.719 104 19% 8%
2013 Cafiero, R Hofstra U 0.395 9.63 20 125 0.377 0.828 133 13% 4%
2013 Moore, D Cypress College 0.328 7.38 27 85 0.281 0.608 74 15% 10%
Keene Swampbats Hitters, 2013-2015

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