1. By Adam Rothstein and Jesse Cox

2. Project description We are going to examine what characteristics, extranalities, and other influences are statistically significant and hold a positive correlation to a player’s batting average.

3. Changing Hitter Performance Finds player’s reach a peak batting average and then slope declines with age Need to consider injuries and minimum at-bats Found that 7-9 years is when batting average peaks. Noticed a flatter decline rein the last two decades most likely due to lax drug policies.

4. Competition and its Effect on Performance Team owners may be driving profits associated with closer wins/losses by equally distributing talent in MLB This is also narrowed by a learning process in which worse players around better players learn Disparity indexes for major leagues across years shows a lower talent gap Did not prove that competition will drive excellence to a peak and then diminish

5. Extranalities of MLB Positive extranality from pitcher to batters Note that giving Batter 1 fewer walks in exchange for more homers and outs willdecrease his on-base percentage but will likely increase his batting and sluggingaverages. So with a good hitter on deck, it is rational for the pitching team to allowthe current hitter to hit for a higher average and for more power in exchange for areduced on-base percentage. This is consistent with the conventional baseball wis- dom that states a hitter is protected by having a good hitter bat behind him and thatthis protection leads to a higher batting average and more power. This also followswhen looking at an extreme case. Namely, if Batter 2 hits homers in every plate appearance, then the rational pitcher will seek to reduce Batter 1’s on-base percent-age at any cost. With a guaranteed homer on deck, a run-minimizing pitcher should be willing to seek an out from Batter 1 even at the cost of risking a home run. Statistically insignificant to assume that better batters on a team will make all batters better

6. Method We are going to compile a data set of MLB players that were active at year 2010 and track percentage increases in batting average. Using characteristics such as age, sex, race, height, weight, marital status, active MLB years. Percentage change in batting rather than decimal change, and age vs years in the league

7. Method Continued We will have to merge and compare data sets with the pandas extension for Python in order to compare and use the data we collect We expect things like age and years in the league as well as height to be significant in increases Possibly merge this with CPI increases for corresponding years or MLB attendance by fans

8. Sources Sommers, P. M. "The Changing Hitting Performance Profile in Major League Baseball, 1966-2006." Journal of Sports Economics 9.4 (2007): 435-40.Sage. Web. 21 Mar. 2016. Horowitz, Ira. "The Impact of Competition on Performance Disparities in Organizational Systems." The Impact of Competition on Performance Disparities in Organizational Systems. University of Florida, May 2000. Web. 22 Mar. 2016. Bradbury, John Charles, and Douglas J. Drinen. "Pigou at the Plate." Pigou at the Plate. Journal of Sports Economics, Apr. 2008. Web. 22 Mar. 2016.