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Wayne Winston

Publications and source records attributed to Wayne Winston.

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The Hot Hand in Actual Game Situations

Streaks of success have always fascinated people and a lot of research has been conducted to identify whether the "hot hand" effect is real. While sports have provided an appropriate platform for studying this phenomenon, the majority of existing literature examines scenarios in a vacuum with results that might or might not be applicable in the wild. In this report, we build on the existing literature and develop an appropriate framework to quantify the extend to which success can come in streaks -- beyond the stroke of chance -- in a natural environment. Considering actual basketball game situations, our results provide strong statistical evidence that the hot hand exists in this setting. Even though our results are based on a sports setting, we believe that our study provides a path towards thinking of the hot hand outside of laboratory-like, controlled environment. This is crucial if we want to use similar results to enhance our decision making and better understand short and long term outcomes of repeated decisions.

stat.AP

A Skellam Regression Model for Quantifying Positional Value in Soccer

Soccer is undeniably the most popular sport world-wide and everyone from general managers and coaching staff to fans and media are interested in evaluating players' performance. Metrics applied successfully in other sports, such as the (adjusted) +/- that allows for division of credit among a basketball team's players, exhibit several challenges when applied to soccer due to severe co-linearities. Recently, a number of player evaluation metrics have been developed utilizing optical tracking data, but they are based on proprietary data. In this work, our objective is to develop an open framework that can estimate the expected contribution of a soccer player to his team's winning chances using publicly available data. In particular, using data from (i) approximately 20,000 games from 11 European leagues over 8 seasons, and, (ii) player ratings from the FIFA video game, we estimate through a Skellam regression model the importance of every line (attackers, midfielders, defenders and goalkeeping) in winning a soccer game. We consequently translate the model to expected league points added above a replacement player (eLPAR). This model can further be used as a guide for allocating a team's salary budget to players based on their expected contributions on the pitch. We showcase similar applications using annual salary data from the English Premier League and identify evidence that in our dataset the market appears to under-value defensive line players relative to goalkeepers.

stat.AP