Measuring Team Chemistry in MLB

Copyright © 2015-2016, David Kelly (kelly.db@gmail.com). All Rights Reserved. 1
Measuring Team Chemistry in MLB
Introduction
There have been several attempts to quantify team chemistry in MLB. In general, these attempts have
been inconclusive because they try to measure the effects of intangibles, like clubhouse camaraderie
and leadership. Indeed, the study of team chemistry has garnered attention because statistics alone
have not been able to explain the excess number of wins (or losses) achieved by some teams. This paper
presents a new approach for measuring team chemistry based solely on won-lost records and player
statistics by adapting techniques used in finance. The results support the claims made by many players
and managers that team chemistry can either be a critical force or entirely absent and unnecessary in
the presence of high overall talent.
An Unsolved Problem
In addition to interviews with players and managers, research to date has focused on the off-field
characteristics of clubhouses. Katerina Bezrukova, an assistant professor at Santa Clara, and Chester
Spell, an associate professor at Rutgers, built a regression model that incorporates three factors –
demographics, isolation and “ego”. Demographics include age, tenure with the team, nationality, race
and position. Isolation can happen when there is too much demographical diversity. The ego factor
captures discrepancies in the caliber of players and their respective salaries. Drawing on research on
conflict and its relationship to group functioning, their model determines the number of “rifts” or
“splits” among these factors using a proprietary “fault-line” algorithm, where the number of splits is
inversely related to team chemistry. They estimate that team chemistry in this form may cause up to
four-win swings. Unfortunately, there is no way to prove the accuracy of these types of models.
Another approach undertaken by Bret Levine and summarized in his recent report, “Measuring Team
Chemistry with Social Science Theory”, examined the leadership aspect of clubhouses. It builds on the
concept of “team cohesion”, defined as a dynamic process that is reflected in the tendency of a group to
stick together and remain united in the pursuit of its instrumental objectives and/or for the satisfaction
of member affective needs1
. Utilizing the link between team cohesion and leadership, Mr. Levine
hypothesized that team cohesion and ultimately performance should increase as leaders reference the
team more, especially at crucial junctures. The study examined pre and post-game comments made by
eight randomly selected leaders on playoff and non-playoff teams during the 2012 season. One of the
conclusions was that there was no significant correlation between the total number of team references
and total wins nor was there negative correlation between self-references and wins. However, leaders
on playoff teams were much more likely to reference the team than themselves after a win. While
intriguing, this work leaves many unanswered questions.
1
Carron, A.V., Colman, M.M., Wheeler, J., & Stevens D. (2002). Cohesion and Performance in Sport: A Meta
Analysis. Journal of Sport & Exercise Psychology, 24, 168-188.

Extensive research on team cohesion has shown that when teams perform better they are more
cohesive2
, in line with the generally held belief among players that winning creates chemistry. Another
important finding is that the performance-cohesion effect is primarily due to commitment to “task”
rather than interpersonal attraction or other clubhouse ‘demographics’. These findings are highly
relevant to the methodology presented in this paper since we restrict the scope of input data to actual
on-field outcomes.
Borrowing from Finance
An MLB team is effectively a portfolio of assets. Like financial assets, players generate a return in the
form of positive or negative incremental wins which can be measured through Win Probability Added
(WPA). The inconsistency or “volatility” of each player’s WPA represents the risk of the investment. The
other important dimension of risk is commonly known as “diversification” or the degree to which the
performances of individual assets offset or move in tandem.
A well-diversified portfolio has lower risk because the assets are less correlated, meaning that losses
from some assets are offset by gains in others and vice-a-versa. Here, we adapt the notion of
diversification, i.e., correlation, to baseball and re-label it “team chemistry”. We can also adapt an
established methodology for calculating it, which basically entails implying a uniform level of correlation
among players from each team’s final won-lost record without altering the actual statistics.
This paper will show that team chemistry can be substantial, in some cases turning a third-place team
into first-place team and propelling an otherwise statistically inferior team through the post-season.
Individual player contributions to team chemistry will also be examined by quantifying the portion of
excess wins or losses attributable to them, adjusted for correlation effects. This is exactly analogous to
portfolio managers examining “alpha” and “beta” metrics, i.e., excess return versus risk, in making
investment decisions. Finally, we will examine the predictive power of the metrics involved through
back-testing.
Calculating Team Chemistry
In order to imply team chemistry from actual won-lost records, we need to know the number of wins
and losses a team would be expected to realize in the absence of team chemistry. This can be calculated
by simulating the season using actual player statistics and assuming zero correlation between player
performances. In other words, we replay each team’s schedule many times, game by game, play by play,
where the outcome of each play is randomly and independently generated based on the statistics of the
players involved. The final won-lost records for each simulation or ‘replay’ and each team are averaged
to arrive at the expected number of wins and losses. This is called “Monte Carlo” simulation, a technique
commonly used in finance when the number of variables is large, as is the case with baseball games
involving many players.
2
Mullen, B. and Copper, C. (1994). The relation between group cohesiveness and performance: An integration.
Psychological Bulletin, 115, 210-227.

To perform the simulations, a proprietary software package called “Henry”, developed by the author,
was used. Henry is comprised of a play-by-play database3
going back to 2000 and a high-performance
simulation engine for regular seasons, post-seasons, series and individual games. The simulator captures
all important aspects of a baseball game, including lineup construction, pitcher versus batter matchups,
fielding and base running capabilities, and bullpen/bench usage (see Figure 1).
Figure 1. Iterative steps in simulating baseball games.
In order to reduce the error of the expected won-lost records to ½ a game, the entire regular season
schedule was simulated 200 times. For each regular season simulation, another 100 post seasons were
simulated in order to calculate each team’s probabilities of advancing through the playoffs and
ultimately winning the World Series.
In Monte Carlo simulation, conditional probabilities for all events that occur within baseball games as
outlined in Figure 1 are derived from actual player statistics, which means that simulated stats converge
to the actual stats by design. Comparing the simulated stats to the actual stats is a means of validating
the model. However, simulated won-lost records do not necessarily match actual won-lost records even
though the stats are the same.
Figure 2 below shows the actual 2015 standings in blue versus the simulated standings calculated by
Henry in yellow assuming zero correlation between players, i.e., no team chemistry. The actual and
simulated tables are split into regular season records and post-season results by series. For the actual
3
Based on information provided and copyrighted by Retrosheet (www.retrosheet.org).
1. For each game, set:
- Starting pitchers from rotations
- Lineups vs. R/L opposing pitchers
- Bullpens: closer, setup, lefty & utility
- Bench players & roles
2. For each inning, simulate batters:
- Pitcher vs batter determines SO,BB,HR,contact
- Batter vs fielder(s) determines hit, out or error
- Fielder(s) vs runner(s) for advances & outs
3. Simulate base runner(s):
- Runner vs pitcher/catcher for SB,CS,PO
- Pitcher & catcher determine WP,PB
- Pitcher balks
4. Simulate personnel moves:
- Pinch hitter/double-switch in NL
- Relief pitcher based on entry rules &
pitch count
5. On game over, record results:
- Winning, losing teams and score
- Simulated team & player stats
- Next spot in the starting rotations

results in blue, the post-season columns show games won and lost for the Wildcard, League Division
Series, League Championship Series and World Series. The simulated post-season results in yellow are
slightly different in that they show the probability of winning and losing in each round.
Figure 2. Actual vs. simulated regular season standings and post-season probabilities with no team chemistry.
Notice that the simulated records for some teams are very close to their actual records but substantially
different for others, like the Royals. Also note that the probability of the Royals winning the World Series
is less than 1% under the zero correlation (no team chemistry) assumption. The opposite is true for the
Nationals, who ended up 11 wins worse than expected and missed the playoffs, which certainly doesn’t
reconcile with the 11.5% probability of winning the World Series shown above.
Since the above simulated results assume no team chemistry, the difference in wins (+15 for the Royals,
-11 for the Nats, etc.) must be due to non-zero correlation between players’ performances. In the Royals
case, more “good” stats happened in key situations and the opposite was true for the Nationals. This
representation of team chemistry is intuitively pleasing since it essentially says that Royals players
tended to execute the task at hand, positively influencing the performances of their teammates, while
the Nationals were fraught with negative dynamics, perhaps exemplified by the infamous dugout melee
at the end of the season.
Having quantified the number of regular season wins attributable to team chemistry, we can solve for
correlation and then use it to adjust the post-season probabilities. Henry simultaneously solves for each
team’s correlation by “fitting” the simulated standings to the actual regular season standings. The
inclusion of correlation in the simulation effectively reallocates players’ stats across game situations

until the simulated won-lost records match the actual won-lost records4
. The final stats are not changed
in the process, thereby maintaining the integrity of the model. The results are shown in Figure 3 below.
Figure 3. Implied team chemistry and adjusted post-season probabilities.
Notice that the simulated regular season won-lost records for all teams (in yellow) now match the actual
records (in blue) very closely5
and the team chemistry column labeled “Corr” is no longer all zeros6
. In
addition to jumping from a third-place team in terms of pure statistical talent to a first-place team, the
Royals chances of winning the World Series leapt from 0.6% to 10.1%. Meanwhile, the Nationals chances
of winning the World Series dropped from 11.5% to 1%. It is also interesting that the Mets’ first-place
standing seemed to have more to do with negative team chemistry among competing NL East teams
than their own positive chemistry. Next, we’ll examine the impact of team chemistry on post-season
series in more detail.
Post-season Chemistry
Case Study #1: 2015 World Series
To see the impact of team chemistry in a short series, we’ll start by examining the 2015 World Series. As
seen in the previous section, the Royals generated substantial positive team chemistry during the
regular season while the Mets were basically neutral. First, we simulate the series assuming no team
chemistry as shown in Figure 4.
4
A one-factor Gaussian copula model, commonly used in finance, was adapted and implemented in Henry. A
copula function is basically a means of combining the marginal probability distributions of individual players into a
multivariate probability distribution representing a team.
5
Residual differences are caused by the combination of simulation error and the imposition of a practical limit on
the number of iterations performed by the fitting algorithm.
6
Remember that team chemistry is cast as correlation ranging from -100% to +100%.

The starting lineups, benches and bullpens versus righty and lefty opposing starting pitchers are listed in
the blue boxes in the center of the screen. The team chemistry settings are shown on the left side of the
screen, outlined by the first red box. The results of the simulation are displayed in the second red box
with the yellow background, indicating that the Mets would be expected to win 56 out of 100 series in
the absence of team chemistry7
.
Figure 4. Simulation of 2015 World Series with no team chemistry.
Re-running the simulation with team chemistry as shown in Figure 5 below flips the expected outcome
to the Royals winning 56 out of 100 series, obviously more in line with what actually happened. The low
number of “sweep” outcomes depicted in the wins distribution table in the lower left and corresponding
chart on the right indicates that the teams were closely matched, which also reflects reality.
Figure 5. Impact of team chemistry on 2015 World Series.
7
Henry automatically switches the home and away teams (and use of a DH) according to the 2-3-2 MLB format.

Case Study #2: 2013 ALCS
Another compelling example of the power of team chemistry is the 2013 ALCS involving the Red Sox and
Tigers. Remember that this was the “Fear the Beards” year for the Red Sox and their success was largely
attributed to team chemistry by their manager John Farrell. Again, we start by setting team chemistry to
zero, as shown in Figure 6. The results indicate that the Tigers would be expected to win 58 out of 100
series, directionally consistent with the prevailing consensus at the time.
Figure 6. Simulation of 2013 ALCS with no team chemistry.
How were the underdog Red Sox able to defeat the statistically superior Tigers? John Farrell’s intuition
was correct – the Red Sox generated substantial positive team chemistry of +29.6% during 2013 to the
Tigers -21.8%. Re-running the simulation with these inputs as shown in Figure 7 reverses the expected
outcome to the Red Sox winning 68 out of 100 series. Indeed, the Red Sox won 4 games to 2.
Figure 7. Impact of team chemistry on 2013 ALCS.

Post-season History
The question is whether these case studies are indicative of a broader pattern. Figure 8 below charts
team chemistry for all post-season teams going back to 2001. While the chart is a bit busy, it is clear that
the vast majority of teams participating in the post-season were characterized by positive team
chemistry. Teams that had little or negative team chemistry relied either on better talent or momentum
shifts arising from the unpredictability or “volatility” of short series.
Figure 8. History of team chemistry in the post-season.
Volatility
As illustriously demonstrated by the “Moneyball” A’s, success over the long regular season success does
not necessarily translate into winning in the post-season. Due to the smaller “sample size”, any number
of scenarios can play out in a short series. The win distribution tables and charts in the case studies
above illustrate the range of possible outcomes. In addition to correlation (team chemistry), the width
and shape of these distributions is directly related to the degree of volatility or what is more commonly
called inconsistency or streakiness of individual players and teams. The tables in Figure 9 rank teams by
batting and pitching volatility in descending order based on the 2015 regular season play-by-play data.

Figure 9. Team volatility rankings by batting and pitching for 2015.
The two volatility columns, “RVOL” and “WVOL”, represent team volatility per at bat of Runs Above
Average (RAA) and WPA, respectively8
. The numbers are not immediately intuitive but the order is
revealing. For example, the Royals were the only playoff team in the lower half of both tables, meaning
their performance was more predictable than the teams above them. Low volatility in combination with
very high team chemistry optimally positioned the Royals for their post-season run.
In contrast, the “Moneyball” A’s ranked in the upper half of the volatility tables during 2002. The 2002
Yankees, who also won 103 games and were likewise eliminated in the League Division Series, were
even more volatile than the A’s. Meanwhile, the 2002 World Champion Angels, like the 2015 Royals, led
the league in team chemistry while ranking in the lower half in volatility.
But volatility is not always a bad thing. The 2006 Tigers were similar to the 2002 Angels and 2015 Royals,
riding high team chemistry and low volatility through the League Championship Series but then losing
the World Series to the Cardinals, who were a relatively high-volatility team. Five mostly unlikely
Cardinals – David Eckstein, Scott Rolen, Yadier Molina, Jeff Weaver and Anthony Reyes – basically stood
on their heads and combined to add over two wins worth of WPA in vaulting the Cards over the Tigers.
In examining particular cases, it is important to remember that volatility and correlation (team
chemistry) merely alter the shape of probability distributions, they do not dictate specific outcomes.
Understanding the probabilities and relevant dynamics allows teams to position themselves for the best
chance of success. In general, statistically superior teams (adjusted for team chemistry) should strive to
8
For player and team volatility, a rolling 12 at-bat window is used to calculate the standard deviation of RAA and
WPA. The 12 at-bat window was chosen since it reasonably approximates of the number of at-bats in a short series
over the course of the regular season.

constrain volatility in order to cluster the range of outcomes around the mean, i.e., winning, whereas
inferior teams should source volatile players in order to generate more “Cinderella” scenarios.
Player Chemistry
The $64,000 (maybe more like $64million) question is how to attribute team chemistry to individual
players, sticking with the restriction that only tangible data be used as inputs. Since team chemistry
represents an average level of performance enhancement or degradation, it stands to reason that
players who performed above/below this level were responsible for exerting upward/downward
pressure on team chemistry. In other words, we look for players who generated WPA above their
expected levels of WPA adjusted for team chemistry.
Figure 10 below shows “Additional WPA” generated by Royals’ batters during the 2015 regular season.
The blue region contains actual stats and the yellow columns display simulated BRAA9
and WPA10
with
team chemistry “turned on”. Therefore, the difference in WPA shown in green represents each player’s
net WPA above or below the team chemistry level. For example, Eric Hosmer effectively produced 3.136
wins during 2015 but even with the boost provided by the Royals’ high team chemistry, his stats equate
to only 0.244 wins, resulting in 2.891 of Additional WPA. Note that Jonny Gomes, often mentioned by
other players for his clubhouse value, also provided a substantial lift to team chemistry even though his
overall stats were not exceptional. Figure 11 contains the equivalent results for pitchers.
Figure 10. Additional WPA for Royals batters during 2015.
9
Batting Runs Above Average (BRAA) is the change in run expectancy taking into account the number of outs and
base runners. Two run expectancy tables are used, one for innings 1-6 and the other for innings 7-9+.
10
WPA is the change in win probability for the exact situation, taking into account the half inning, number of outs
and base runners.

Figure 11. Additional WPA for Royals pitchers during 2015.
Like Eric Hosmer, Wade Davis performed substantially above expectations. Granted, relief pitchers
benefit from the convexity or ‘leverage’ inherent in WPA, he was still over three wins better than Greg
Holland in a similar role. The other pitcher that jumps out is Johnny Cueto. Even though he generated
over three wins in terms of WPA, he should have been about three wins better given his stats and
adjusting for the Reds’ negative team chemistry and the Royals’ positive team chemistry.
Expanding the analysis to the entire league, Figure 12 ranks the top 15 batters with the highest
Additional WPA (ADD WPA), normalized to 400 plate appearances. The list is not exactly a who’s who of
offensive production, exposing the limitation of this metric. Michael Taylor led the league in ADD WPA
but his Actual WPA (ACT WPA), shown in the third column of the table, was approximately zero. The
same pattern applies to pitchers (see Figure 13).
Figure 12. Additional WPA batting leaders for 2015.

Figure 13. Additional WPA pitching leaders for 2015.
As reflected in the above tables, ADD WPA is useful for identifying players who contribute to team
chemistry but it is not a good measure of overall value. ACT WPA is a better measure of total value but it
can be inflated or deflated by team chemistry. The good news is that we can adjust ACT WPA for team
chemistry to make it more comparison-friendly.
The adjustment is calculated by simulating WPA with and without team chemistry and then taking the
difference. The portion of the player’s WPA attributable to team chemistry can be seen as the extra
“juice” (or lack thereof) provided by teammates. The tables in Figures 14 and 15 below rank the top 15
batters and pitchers by Adjusted WPA (ADJ WPA), again normalized to 400 plate appearances.
Undoubtedly, these names are more in line with expectations in terms of relative value.
Figure 14. Adjusted WPA batting leaders for 2015.
Figure 15. Adjusted WPA pitching leaders for 2015.

The adjustment for team chemistry can be pronounced, as in Bryce Harper’s case. The Nationals’
substantial negative team chemistry resulted in about a 20% reduction in his WPA, information his agent
could probably use. Batting and Pitching RAA can be similarly adjusted for team chemistry. Descriptions
of all of the metrics shown in the above tables are as follows:
 ACT RAA – Actual aggregate RAA generated by the player (negative for pitchers since it
represents runs subtracted).
 ACT WPA – Actual aggregate WPA generated by the player with no adjustments.
 ACT RAA/VOL – ACT RAA divided by the player’s volatility to create a risk-adjusted return
measure, analogous to the “Sharpe Ratio” used in finance.
 ACT WPA/VOL – ACT WPA divided by the player’s volatility to create a risk-adjusted WPA
measure.
 ADD RAA – Additional RAA generated by the player over simulated RAA adjusted for team
chemistry to isolate the player’s contribution to team chemistry in terms of runs.
 ADD WPA – Additional WPA generated by the player over simulated WPA adjusted for team
chemistry to isolate the player’s contribution to team chemistry in terms of wins.
 ADD RAA/VOL – Risk-adjusted ADD RAA.
 ADD WPA/VOL – Risk-adjusted ADD WPA.
 ADJ RAA – ACT RAA adjusted for team chemistry to create a more comparison-friendly RAA.
 ADJ WPA – ACT WPA adjusted for team chemistry to create a more comparison-friendly WPA.
 ADJ RAA/VOL – Risk-adjusted ADJ RAA.
 ADJ WPA/VOL – Risk-adjusted ADJ WPA.
As in finance, a rich set of metrics like these are needed to analyze and predict risk-adjusted returns,
especially in light of the substantial investments teams make in players. The robustness of these metrics
can be evaluated through back-testing.
Predictability
The goal of back-testing is to determine the predictability of a model using historical data but without
the benefit of hindsight. For example, we can test the hypothesis that ADJ RAA and ADJ WPA are good
predictors of performance for position players and pitchers, respectively, by going back to the start of
each season, substituting players with higher projected ADJ RAA or ADJ WPA, and then observing the
actual number of wins they ended up producing versus the players they replaced. Acceptance or
rejection of the hypothesis is based on examining the differences in wins over several seasons.
The basic steps in back-testing are as follows:
1. Choose strategic metrics to test for position players and pitchers, e.g., ADJ RAA and ADJ WPA.
2. For each team, identify the roster changes made by the team going into the season being
tested.
3. For each roster change, find the best replacement player based on the strategic metrics using
historical stats up to but not including the season being tested. Only players with comparable

value can be chosen, i.e., you can’t replace a journeyman outfielder with Mike Trout or a spot
starter with Zack Greinke.
4. Sum the differences in wins (ACT WPA) between the proposed replacement players and the
actual players for the season being tested.
5. Repeat the above for all teams over many seasons to substantiate conclusions.
Figure 16 displays the results of this particular back-test. The other parameters, in blue at the top of the
screen, are as follows:
 Start Season – First season to back-test.
 End Season – Last season to back-test.
 Hist Seasons – Number of historical seasons to use in projecting the strategic metrics.
 Proj Type – Projection methodology (in this case, a Weighted Average of the previous two
seasons).
 Min Seasons – Minimum seasons required for eligible replacement players.
 Min PA – Minimum plate appearances during each historical season for eligible replacement
position players.
 Min BF – Minimum batters faced during each historical season for eligible replacement pitchers.
 Batting Stat/Batting Diff – Metric to use along with acceptable tolerance in determining
comparable replacement position players.
 Pitching Stat/Pitching Diff – Metric to use along with acceptable tolerance in determining
comparable replacement pitchers.
 All Players – Indicates whether all players, regardless of contractual status, were considered as
potential replacements, as opposed to only those who switched teams.
Figure 16. Back-testing the predictability of ADJ RAA for position players and ADJ WPA for pitchers.

The table contains the gain or loss in wins each team would have had with the recommended
replacement players for each season from 2003 through 2015. Total win differences by season and team
are given in the last row and column of the table. The grand total of +518.128 wins indicates that ADJ
RAA and ADJ WPA are indeed good metrics to use in choosing position players and pitchers. This
conclusion is further reinforced by the small number (5) of negative season and team totals. It is also
interesting to observe which front offices have outperformed and underperformed with respect to these
metrics. For example, the results call into question the recent moves made by the Red Sox while
confirming the strategies of the Cubs, Giants and Pirates over the past few seasons.
The Red Sox made two very big moves before the start of the 2015 season, investing $95 and $88million
in Pablo Sandoval and Hanley Ramirez, respectively. Drilling down into the back-testing results, Figure 17
shows the list of players with higher projected ADJ RAA at the start of 2015. Reading left to right, the
first section of the screen shows the players who occupied these positions at the end of 2014, followed
by the aforementioned free agents who replaced them in 2015, and finally the list of recommended
alternative players based on pre-2015 projections of ADJ RAA. The last column “’15 dWPA” represents
the gain or loss in wins if the alternative players had been signed instead.
Figure 17. Players with higher projected ADJ RAA than Hanley Ramirez and Pablo Sandoval.
Cross-referencing this list with players with the highest projected team chemistry value, as measured by
ADD WPA, two names re-appear, Jayson Werth and Nolan Arenado (see Figure 18).
Figure 18. Players with higher projected ADD WPA than Hanley Ramirez and Pablo Sandoval
If the Red Sox had signed Jayson Werth and Nolan Arenado instead of Hanley Ramirez and Pablo
Sandoval, they would have been approximately 5 wins better as tabulated in the last column, but that’s
not the whole story. They would have also gained 3.5 wins worth of positive team chemistry, which is
the net difference in ADD WPA generated by these four players during 2015.
Since 3 additional wins translated into 12.3% of team chemistry for the Red Sox in 2015 (from Figures 2
& 3), another 3.5 wins would potentially raise it to 26.6%. The total impact can be measured by
simulating the Red Sox 2015 regular season schedule with the two replacement players and higher team

chemistry value, as illustrated in Figure 19. The bottom line is that the Red Sox could conceivably have
won 90 games and made the post-season. It may have cost less too.
Figure 19. 2015 Red Sox with Nolan Arenado and Jayson Werth instead of Pablo Sandoval and Hanley Ramirez.
Conclusions
Performances of players may be correlated through team chemistry, injecting leverage that can amplify
upside wins or downside losses. The magnitude of the impact of team chemistry in relevant cases is
consistent with assertions made by several prominent players, including John Lackey, Jake Peavy, David
Price and Brandon McCarthy. The volatility or inconsistency of players and teams is a closely related
additional source of risk.
As portfolio managers, MLB front offices are challenged with assembling rosters capable of generating a
target number of wins with an appropriate amount of risk given available capital. Small market teams
may need to source risk in order to compete with teams that have more resources, while teams with
statistically superior players are incentivized to minimize uncertainty. As former player and manager Bud
Black put it: “Very talented teams don't necessarily have to have [team chemistry]. But with teams not
as talented, it can help you in terms of momentum, confidence, playing together." Since each team is
effectively “long” their own portfolio of players and “short” opponents’ portfolios, they should also
understand other teams’ risk and return profiles.
Achieving all of the above is predicated on a detailed and unbiased understanding of the performance
dynamics of players and teams. This can be accomplished with the type of simulation and back-testing
infrastructure implemented in Henry, which allowed us to quantify the chemistry related components of
the Royals success espoused by their players and evaluate ways of replicating it. Predictability of
individual player performances, especially in combination with other players, has been one of the
biggest challenges. The best way to identify metrics for predicting performance is through back-testing.
Potential rosters can then be simulated under various team chemistry assumptions to see how the team

would fare against the rest of the league during the regular season and post-season in constructing an
optimal portfolio.
About the Author
David Kelly has spent over 20 years on Wall Street in quantitative research, derivatives trading and risk
management. He has run quantitative research groups for global banks and financial software
companies and has a track record of modeling and technology innovation. At Citigroup, he was part of a
core team that created the Global Portfolio Optimization desk to actively manage the bank’s
counterparty exposures. At JPMorgan Chase, he led the development of what are now industry-
standard credit risk management models. He also helped launch a prominent hedge fund and developed
the firm’s analytical systems. A graduate of Colgate University with a degree in Economics, Mr. Kelly
went on to study graduate statistics at Columbia University and has completed the actuarial enrollment
exams. He was also a starting pitcher for Colgate and is a lifelong baseball fan.

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