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.