Just before the 2019 MLB trade deadline Nick Castellanos was traded from the Detroit Tigers to the Chicago Cubs. With the trade, Castellanos moved from what would ultimately be the worst team in baseball in 2019 to a Cubs team making a postseason push and finishing with 37 more wins than Detroit. Castellanos' wOBA increased by 0.077 and his wRC+ increased from 105 to 154. It appears that moving to a team filled with more talent and more motivation to win caused his performance to improve but is that true for all players such that an increase in performance could be predicted? This post will use all trades from the 2017-2019 MLB trade deadlines to investigate this idea.
This project required more manual data collection than I have done in the past and I started by getting all of the players who were traded in "trade deadline deals" from 2017 to 2019. To do this I relied heavily on sports reporting and its accuracy. In total there were 49 position players and 78 batters to analyze.
Once a list was collected I needed to gather performance statistics from each player before and after they were traded. Fangraphs.com had all the information I needed and I selected two statistics for batters and two for pitchers to use in the analysis. For batters I used wOBA as a general measure of a player's hitting ability and wRC+ in an effort to control for changes in park effects. The statistics I used for pitchers were FIP- and SIERA. Both stats measure a pitcher's underlying skill level and adjust for park factors.
I also collected each team's final winning percentage in each season as a way to quantify if a player moved to a better or worse team and by how much. Using end of season winning percentages is a rough estimate since the player may effect that outcome themselves but it was significantly easier than collecting records before and after each trade for each player given I was collecting data manually.
The analysis on the data required a difference between the player's performance statistic to be compared to the difference in the winning percentage of the two teams he played for that season. My assumption was that an increase in team winning percentage would result in an increase in player performance.
Example Calculation from Nick Castellanos in 2019:
Old Team: Detroit Tigers, 0.292 Win %
New Team: Chicago Cubs, 0.519 Win %
wOBA with Tigers: 0.331
wOBA with Cubs: 0.408
Change in Win % = 0.519 - 0.292 = 0.227
Change in wOBA = 0.408 - 0.331 = 0.077
These calculations were done with each player and statistic in python (https://github.com/koby-a-close/PerformanceChanges_AfterTrade) and then plotted and checked for any trends using ordinary least squares. The following slideshow shows the results for all four statistics with the OLS line.
None of the statistics used showed any trend or statistical significance. The R^2 values were:
Based on this analysis there is no predictable change in performance when a player is traded mid-season based on the record of the new and old teams. This makes a lot of sense mainly because playing for the Cubs instead of the Tigers does not change the true skill level of Nick Castellanos and the same is true for all players. Park factors could be significant but I attempted to control for those effects in my statistic selections.
Other factors also play into the equation that are much harder to analyze like clubhouse fit, increased pressure to perform, and personal adjustments to new cities. Additionally, half a season is a very small sample size and the noise in data was significant. Practically this means that predicting a player's performance after a trade is best done using projections for the season using large amounts of historical data. Considering non-data driven factors is an art and will depend on the individual involved.
More statistics for individuals and teams could be tested but I doubt any significant would be found. Good players tend to play well and poor players tend to play poorly.