1. Progress Report #2 By Adam Rothstein and Jesse Cox

2. Topic Changes We want to try to isolate the effect of a player’s teams’ experience on their personal performance in the league Use % increase in BA over two years as dependent Variable and team stats for independent

3. Predictions New or young players who are picked for a team with more experience will see sharper increases in performance Teams that went further in post season will see sharper improvement Teams that may not have high wins but other stats that are high may also increase player performance

4. Possible Applications Job placement for new hires Predicting new player performance in MLB Class placement for students Class placement with instructors

5. Data Sample Selection – Players under 25 in 2013 – Have a BA for a team/not traded – Also appeared in 2014 MLB games

6. Data Variables – Player’s change in BA (dependent) – Team’s average batting age (independent) – Team runs/game (independent) – Team’s runs overall (independent) – Team’s rank in their league (independent) – Team’s win % (independent)

7. Data Post season variable – Looked at the post season stats of all teams for the past 5 years – A team received: A 1 if they did not reach playoffs 2 if they reached playoffs 3 if they made a world series appearance 4 if they won the world series – Score is an aggregate of last 5 years

8. Summary Statistics BA '13 BA '14 BA Change T BatAge T R/G T R \ mean 0.233545 0.228174 0.117803 28.115702 4.022066 651.809917 std 0.064098 0.062167 0.666229 1.333667 0.324368 52.637014 min 0.063000 0.034000 -0.877698 25.700000 3.300000 535.000000 max 0.400000 0.400000 3.069767 32.500000 4.770000 773.000000 T BA T Rank Post Season T Win % mean 0.250537 3.140496 6.479339 0.489529 std 0.011092 1.337322 2.061954 0.056619 min 0.226000 1.000000 5.000000 0.395000 max 0.277000 5.000000 14.000000 0.605000