• It is slow, almost painfully slow at times. With everyone possibly using it right after this article is released, it may be even slower. Right now, it takes over 1 minute to process one pitcher for just one year. If it is too slow over the next day or so, try it again when less people may be using it.
• Predicting possible injures is an imprecise science. Two pitchers could have almost identical values, but one may need Tommy John surgery and the other one wouldn’t. All I have done is make data available on traits which have been previously known to lead to injuries.
• Directions: Select a starting pitcher (tool only really works with starters because a minimum number of pitches needs to be thrown) and date range. Press submit. Next will come up a most common pitches chart for the pitcher. Pick the most common fastball (to use FA if going back to 2009). Press Submit and have a drink or take a nap because this may take a while. The results will eventually appear.
• The first graph is pretty simple. An average velocity graph for the selected pitch with a 5 game average curve.
• The second graph measures late game consistency. A 100 value is an inconsistent pitcher and 0 value is a consistent pitcher. I got the values by looking at pitchers with major arm issues in a season and pitchers without arm issues in a season. Then, I compared one group of pitchers to the others over the last 10 fastballs thrown in each game. Velocity, release points and break were examined using logistic regression. In the end, I got a formula which detects inconsistencies. The exact cause of the inconsistent is not outputted. The user will need to go look at the game data to find the pitcher’s exact issue. Note: Pitchers, like Bruce Chen, who have two distinct release points, will have all their values near 100. I have not been able to work out this problem yet.
• The final graph is the pitcher’s Zone%. A value under 47% (baseline value on graph) means the pitcher had issues throwing strikes and is more likely to be injured.
• In the near future, I will be going back and looking at how the values can be applied to other pitchers and situations.
Let me know of any issue or ideas for improvement.
Here is some work from Clint Hulsey on home and away data:
- The Mariners Rotation Batted Ball Rates: Home/Road Splits
- Batted Ball Differences In Colorado And Texas-Off The Radar
- Can We Quantify the “Marine Air Effect” at Safeco?
Additionally, Mike Podhorzer and Chad Young have been writing a series on the Quest to Predict HR/FB Rate (5 parts so far):