How to Pull and Read Lewan Injury History Data
If you're building a team model or just trying to figure out why a player's output dropped last season, you need to understand Lewan Injury History. It's not magic, and it won't make you rich if you just paste it into a spreadsheet and pray. The data exists, it's messy, and getting it right takes a few steps most people skip. The Lewan framework tracks injuries by player, club, and time window. It categorizes missed matches, severity tiers, and recurrence patterns. The original paper came out around 2020 from researchers at Lancaster University. It's been adopted by a handful of clubs and analytics firms, but public access to clean versions is basically zero. What you'll find online is either scraped approximations or people copy-pasting table rows without understanding what the columns actually mean.
My Lewan Injury History Workflows
I run this for semi-pro and lower-league clubs, mostly because their staffs don't have six-figure budgets for Opta or StatsBomb. Here's how I actually get it done. First, you need raw match data. Not the headline stats, the actual match logs. You can scrape the English FA site for youth and semi-pro fixtures, or pull from the Football Data Co's free tier if you only need top-flight data. From there, you map each player's appearance to every match in the season. Any gap where a player should have been available but didn't appear gets flagged as a potential injury absence. That's the part nobody mentions. You're not pulling an injury record from a database. You're inference-mining it. You cross-reference social media posts, substitute timings, warm-up appearances, and training ground reports to confirm whether a missing game was actually injury-related or just tactical rotation. A player who sits out two games in a row and returns as a 70th-minute sub is probably carrying a knock. A player who doesn't travel for a cup match? Might be rest, might be injury. The Lewan Injury History method accounts for this ambiguity by weighting confirmed absences higher than inferred ones.
Once you've built your timeline, you calculate three core metrics: missed match percentage, recurrence rate within 30 days, and severity score (games missed divided by total squad games available). These feed into a risk model that estimates how likely a player is to miss the next fixture based on their recent load. Here's where it gets ugly. I spent three weeks last year trying to validate this for a Championship side. Their medical staff logged injuries in one system, their performance analyst used another, and the league's official data had errors in about 15 percent of entries. My initial model was off by nearly double the actual missed games because I was counting rehabilitation appearances as full-match absences. A player coming back from a hamstring strain might train with the squad and sit on the bench for 90 minutes, then not get subbed on. That's not an injury absence in the Lewan framework, but my scraper was flagging it as one anyway. The fix was adding a manual review pass. I built a simple script that exports all flagged absences into a CSV, sorted by player and date, and let the club's physio team mark each one as confirmed injury, tactical rest, or data error. That alone cut false positives from about 40 percent down to under 8 percent. It added two hours of work per player per season, which sounds like a lot until you realize you're saving someone from starting a 32-year-old with a recurring knee issue against a team that presses high.
Get the Full Details
There are tools that claim to automate this. InjuryReport.io, FootballInsights.injury, and a few GitHub repos with names like "premier-league-injury-tracker." They work for a quick look. They fall apart when you need player-level granularity because they rely on publicly reported injuries, which clubs consistently underreport. The official source for Premier League injury data is still whatever the clubs choose to tell the media on a Tuesday afternoon. If you want something better than scraping, the Lancaster University group does offer a contact form for research collaboration. It's not a download link. It's an academic arrangement. Most people who email them get a polite reply asking for their institutional affiliation. If you're independent, you're on your own for the data pipeline, but the methodology is published open access. The real value of Lewan Injury History isn't the scores. It's the pattern recognition. I've seen it flag a striker who kept getting "minor" knocks three weeks before a serious tear. The data didn't predict the injury, but it predicted the risk trajectory. That's what matters when you're deciding whether to sell a player in January or push him through a congested fixture list.
The downside is that the model assumes consistent reporting standards across clubs and leagues. Once you start comparing data between the Belgian First Division and the Scottish Premiership, the variance in how injuries are logged makes cross-league comparisons almost meaningless. The severity scores will look similar because the formula is the same, but the underlying data quality varies wildly. I stopped trying to normalize across leagues after I spent a month building a comparison matrix that turned out to be garbage because one league counts muscle strains as injury absences and the other doesn't. For domestic use only, stick to one league and one season at a time. The framework holds up. Just don't treat it like a crystal ball.
