This is a review of the baseball research conducted by Clayton Graham, applying monte carlo simulation and production function techniques.
As long as humans have been competing against each other, others have wagered regarding the outcome. Sports and betting go hand in hand across time, sports, and societies. In order to profit in the baseball marketplace, baseball game modeling and analytically based gambling are amalgamated. Investment gaming’s objective is to maximize the expected profits of a baseball season, following investor risk guidelines.
The ultimate goal in baseball is to score runs. In order to score runs a batter must first reach base. Therefore, the production function is built using singles, doubles, triples, home runs, and base on balls as inputs. The output is the number of runs per out. Runs per out are incorporated rather simply the number runs scored as this effectively cancels out any effects caused by a varying number of innings between games. A formula for expected winning percentage is created as a combination of density functions related to runs scored and runs allowed in a Pythagorean Theorem. The formula is run through a Monte Carlo simulation to create an accurate representation of the distribution of winning percentages and winning margins. The results then need to be adjusted for batter pitcher matchup and ballpark factor. The ballpark factor measures the difference between runs scored in a team’s home park and road games.
The betting line determines the cost of the bet, the resulting payoff, and an implied probability of winning. The most common form of betting on baseball is money line bets. Bringing in the idea of economic consequences involves including the money line’s cost, payouts, implied probabilities of winning along with the production function’s expected scoring, and the game’s predicted probability of winning.
Several conclusions are drawn after running the model. Only a small percentage of games are worth an investment. The overall winning percentage is typically 68% and the average return on capital is approximately 35%.
The methods discussed here can be used by coaches and analysts to determine the value of various batters and pitchers. They can also help identify strengths and weaknesses in various teams in the league, which provides tools for developing strategies to improve one’s team and gain a competitive advantage against opponents. Finally, the methods included here would aide coaches in deciding which players should be included in a game’s lineup depending on the opponent and the ballpark where the game is being played.
Analytics Used: Production Function, Pythagorean Theorem, Monte Carlo Simulation
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