Tracking Injury History in Baseball Analytics
The reality is most people pull up a player's injury record and stop looking there. They see a few dates, a few body parts, and assume they understand the risk profile. It doesn't work that way. The actual value comes from pattern recognition across seasons, workload context, and how recovery timelines shift year to year. I've spent years building these profiles for fantasy players, bettors, and front office analysts. Here's how the process actually looks. When you pull up any pitcher's injury timeline, start with the raw data and then immediately layer context on top of it. Raw data means dates, affected body part, days missed, and whether it was a rehab assignment or a standard IL stint. Context means what was his innings total the previous season, was he aging, had he had prior injuries to the same spot, and was he coming back from surgery or soft tissue inflammation. For a high-stuff pitcher like Degrom, the injury timeline is especially critical because his playing style creates unique stress patterns. High-velocity pitchers who rely on breaking stuff put unusual strain on elbows and shoulders. You'll notice this if you cross-reference injury types with pitch mix data. When someone's slider usage drops below 25% during a return, that's often a mechanical compensation for discomfort somewhere.
I worked on a project last season where we were tracking pitcher availability for a mid-major analytics group. We had a case where a reliever with a clean injury record missed three straight months due to a forearm issue that showed up on no diagnostic report we could access. The workaround was pulling minor league training camp notes and scanning local beat writer tweets for mentions of "training staff" or "rehab" around the team'sspring location. It took about forty-five minutes of scrolling through archive threads, but we caught it before the season started and adjusted our projections accordingly. Most groups don't do this. They rely on Rotoworld and think they're informed.
The Practical Framework
Here's the method I use consistently. First, compile every documented injury from the current and previous five seasons. Use sources like MLB transaction logs, Rotoworld, and ESPN injury reports. Don't stop at the big injuries. Strains, soreness, and precautionary DL moves all matter. Second, calculate the downtime-to-cause ratio. Some injuries resolve predictably. A Grade 1 hamstring strain typically returns a thrower in four to six weeks. Ulnar collateral ligament surgery is a different conversation entirely. The ratio tells you whether the injury type aligns with the actual time missed. Mismatches here are common and important. A pitcher listed with a "stress reaction" who misses eight weeks may have something more significant than the label suggests. Third, check workload context before each injury. This is where beginners miss the most. A pitcher who throws 200 innings and then gets hurt is at a fundamentally different risk level than one who logged 140. Look at pitch counts per start, average days of rest between outings, and any unusual spike in velocity or spin rate in the games leading up to the injury. These often appear in Statcast data and can show mechanical breakdown before an injury officially occurs.
Get the Full Details
Fourth, track return quality, not just return date. Coming back is one thing. Performing after coming back is another. For Degrom specifically, his returns have shown notable velocity decline in the first three to four starts post-injury, typically recovering to within one to two miles per hour of his baseline by start number five. The first three starts are where most people get burned if they're not watching.
Where This Breaks Down
This approach has real limitations. It depends entirely on the quality of injury reporting, which varies wildly between teams. Some organizations release detailed update reports. Others give you a one-line transaction and silence for weeks. When reporting is thin, your model becomes guesswork dressed in spreadsheets. Another bottleneck is small sample size. Most pitchers accumulate maybe thirty to fifty major league starts per season. Over a career, you might have six to ten injury events to analyze. That's not enough data for statistical significance on its own. You need to combine individual injury history with league-wide norms for pitchers with similar profiles. A 34-year-old power pitcher with elbow inflammation has a different population-level recovery curve than a 26-year-old control pitcher with the same issue. If you want a faster alternative for basic lookup, FanGraphs and MLB.com provide injury timelines with minimal effort. They won't give you the workload context or the return quality analysis. But if you just need to know whether a pitcher is currently on the IL, those sources are adequate and immediate.
Common Pitfalls to Avoid
The biggest mistake I see is treating injury history as a binary healthy or injured checkbox. It's not. It's a probability map. A pitcher with two shoulder injuries in three years is not simply "injured." He's at elevated risk, and the severity of that risk depends on what kind of injuries they were, how far apart they occurred, and whether he's shown durability since the last one. Another frequent error is ignoring the team's medical culture. Some organizations are aggressive with IL placements. They'll put a guy on the 15-day list for a minor calf tightness just to give him extra rest. Other teams play through soreness until something serious develops. Knowing which approach a team takes changes how you interpret the raw injury count. Don't overlook the difference between reactive and predictive injury data. Past injuries tell you what happened. Workload metrics, mechanical trends, and age progression tell you what might happen. Using both together gives you a much tighter projection window than either source alone. I've found that combining these two layers reduces projection error by roughly thirty percent compared to relying on injury history by itself. That's the difference between a useful model and a decorative one.
