2022 Spring Edition: Volume 2

The projects represented in Volume 2 were completed by rising students who completed our summer high school programs Moneyball Academy and Moneyball Academy Training Camp in 2019. We hope you enjoy reading them!


Determining the Nastiest Pitcher in 2019 MLB (2019)
By Jacob Hahn, Crash Collier, Theodore Barton

We determined which pitching stats affect success of pitcher and from these determine the pitchers who excels the most in those stats. Pitch movement? Spin Rate? Pitch Velocity? Whiff Rate? The data set was obtained from Statcast at Baseball Savant and included all MLB pitchers in 2019.  Who is the nastiest pitch in 2019 MLB?

The Importance of the Serve; Shining Through a Lack of Quality Data (2019)
By Eve Pennington, Zeke Kelz, Rita Li, Jenny Song

In men’s tennis, the serve is a critical element of the game.  In this study, we evaluated various metrics of serve data to create a predictive model demonstrating how serve speed and ace percentage can be forecasted using player height, weight, and handedness information.  We analyzed tennis serves from the Men’s Australian Open from 2004-2014.

Judge Nationality Bias in Figure Skating (2019)
By Alexa Jin, Kexin Ding

Figure skating outcomes are determined by a panel of judges from different nations.  There are five judged elements per performance and nine judges per element.  Nationality bias of judges has been a documented issue in many Olympic sports.  In this study we used the p-value test to analyze nine judges’ nationality bias in the 2017 World Figure Skating Championship.  The data set included the 32 performances in the in 2017 World Figure Skating Championships, which is 1,440 judged elements.  Judge nationality bias is observed across a range of county nationalities.


Understanding the Importance of Each Point in Tennis (2019)
By Jiaxi (Barry) Wu, Jake Federman

Tennis matches are won by winning games, and games are won by winning points.  A winner of a match will lose many points. From career grand slam performances from 2002 to 2019, we determined which statistics correlate with winning percentage and created a model to optimize these factors. 


Relative Age Effect in Elite Soccer, Consider Females
(2019)
By Bryan Zhang, Joanna Yang, Roma Gandhi

How does the relative age effect impact elite soccer players internationally? This work disproves a common misconception of the relative age effect (RAE) in youth and professional soccer and identifyies further reasons behind this bias. 


Expected Value of an NFL Fantasy Draft on a Positional Basis
(2019)
By Michael Hammer, Michael Butshansky, Dylan Oberst

In NFL fantasy football individual offensive players (QB, RB, TE, WR) are selected in a draft before each season, and these selected players generate points as the season progresses.  In this work, 2017 NFL data is used to predict 2018 fantasy performance.  Analysis is conducted on individual offensive players from position of QB, RB, TE, WR, and team defenses. The relationship between draft position and fantasy points per game is modeled and used to evaluate past fantasy drafts and determine fantasy draft strategies.


The Rich Hill Index: Optimizing a MLB Pitcher’s Repertoire
(2019)
By Alex Corb, Jeremy Swack, Henry Gutkin

MLB pitcher Rich Hill transformed into an elite pitcher by increasing the usage of his one-of-a-kind curveball, leading to his eventual success in the MLB. Quality of Pitch (QOP) is a pitching metric developed by Jason Wilson that takes into account velocity, pitch break, location, lateness of break, while excluding the outcome. In this analysis, we use data from 2019 MLB pitchers with a minimum of 450 pitches to create a Rich Hill Index (RHI).  


Combine Performance vs NFL Success Football
(2019)
By Adam Weiss, Ronak Thakker, Jatin Nayyar, Thompson Schmiel

NFL draft prospects are assessed over a variety of physical measures at the annual NFL combine. To varying extents, NFL team use these measures to make draft decision on individual players. We explored if an NFL draft prospect’s combine statistics correlate to their future NFL success. If so, can these combine numbers predict how well a draft prospect will perform in the NFL. Using our model, we predict the successful in the NFL of players in the 2019 draft.