Baseball free agency is an exciting time for players, agents and The Sports Resource. Analytics can highlight a player’s achievements, demonstrate and quantify his value, and show his contribution to the club. Unlike arbitration – a process restricted by specific criteria – free agency packages can also focus on what’s ahead with statistical projections.
Since free agency usually involves changing teams, this may add another level of complexity. But the insight gained from this research pays off both immediately and down the road, when it comes time for the next contract. Context impacts statistics far more than many realize, and joining a new club can dramatically change it.
These four key questions address areas where sports analytics can have a major impact beyond the basic components of a free agency package.
1. How will the level of competition affect your player? We elaborated on the AL East's impact on player statistics in the January 2010 issue of The Sports Resource Newsletter. Fortunately, methods exist to predict the impact of competition changes on individual players.
2. In which ballparks would he excel? Actual performance in different ballparks can be valuable. However, players may lack enough plate appearances to make those statistics meaningful.
Park factors give us an indication of how a player will perform when changing stadiums. Everybody knew that Adrian Gonzalez would benefit from leaving PETCO Park for Fenway Park. But it isn’t always that easy. Park factors vary from year to year largely due to weather patterns. Complicating matters further, many stadiums help certain types of hitters more than others. For example, Minute Maid Park is great for right-handed pull hitters with power, but not nearly as great for lefty home run hitters.
3. What impact will a new lineup have? Hitting in a strong batting order has a positive effect on context-dependent statistics like RBI and runs scored, as well as batting average to a lesser extent.
4. Are the potential new teams over or undervalued? Every player wants to win, so this last question is vital. Team records can prove misleading. So it’s better to examine Pythagorean won-lost records, which project winning percentage based on runs scored and allowed. For example, the Astros went 76-86 in 2010 and finished strong. That made them look like a team poised to turn the corner. However, their Pythagorean record was just 68-94. This makes their poor 2011 performance less surprising.