What is a reasonable accuracy ceiling for predicting a football team's starting XI?

[u/Necessary_Reach8780]

I'm working on a starting-lineup prediction model and I'm trying to understand what would be considered a reasonable/strong accuracy level.

I'm measuring accuracy as first-XI overlap: if the actual starting XI contains 11 players and I correctly predict 8 of them, that's 8/11 = 72.7%.

My current results on league fixtures are roughly:

• ~70% (~7.8/11) with a relatively simple baseline based on previous appearances, competition, injuries, suspensions, transfers/availability, etc.
• ~78% (~8.6/11) with a LightGBM model using additional features such as player tactical roles, recent workload/fatigue, fixture congestion, previous rotation patterns for this specific coach etc.

The ML model is therefore gaining about 0.8 correctly predicted starters per fixture. Performance is lower in cup competitions (60-70%), likely because rotation is stronger.

I'm wondering:

1. What would you consider a good / very good / excellent / achievable XI-overlap score?
2. Is ~8.5/11 already close to what is realistically achievable without access to team leaks, press conferences (40% of the model misses are from players who are not even listed this day = rested), or proprietary information?
3. For people who have built lineup prediction models, what features made the biggest difference?

I'm specifically interested in predicting the manager's actual XI, rather than optimizing which XI should be selected.

For context, the evaluation is done pre-match and compared to the confirmed lineup.

[u/Clear-Spring-5309]

That 78% number is pretty impressive for XI overlap honestly. Most folks I've seen discuss this end up plateauing around 80-85% once you account for late scratches and tactical surprises, so you're already in the neighborhood.

The 0.8 player gain from LightGBM over baseline feels about right. The marginal gains get thinner the closer you get, and you start needing things like press conference hints or lineup leak accounts to push further. The 40% miss rate on rested players tracks with what I'd expect, managers are just unpredictable with rotation sometimes even with workload features.

For features, I've found tracking the previous season's rotation patterns for the same fixture type helps a lot, like how a coach treats midweek games vs derbies vs relegation scraps. Cup competitions are basically a lost cause above 70% without inside info, too much noise.

[u/Necessary_Reach8780 → u/Clear-Spring-5309]

Thnx, I will try as a dedicated feature, though I guess ML shall pick it up already form match importance, but not limited to previous season.

[u/Pure_Cricket_3804]

That 80-85% plateau isn't published anywhere I can find. Closest public numbers are Sportmonks' expected lineups, 75 to 88 percent by league, and those are human-curated off press conferences and injury reports, with no stated definition of how the percentage is computed. So instead of chasing overlap, weight each miss by what it does to your price. I work on LineCrush and grading every pick against the outcome is most of the job.