Built a stat model that finds mispriced player props on Kalshi — here's today's signals
MLB heavy right now since the NBA doesn't have games everyday
u/Dizzy-Maintenance120 — 6 days ago
u/Dizzy-Maintenance120
▲ 0 r/BasketballGM
Built a Z-score model that finds +EV NBA player props on Kalshi
Been playing Kalshi NBA player props for a while and got frustrated trying to manually spot when a line was off. So I built something to do it systematically.
The idea: if a player's rolling 10-game distribution says they hit over X points 65% of the time, but Kalshi's implied probability is only 41%, that's a gap worth betting.
Z-score against each player's last 10 games, blended with empirical frequency, then Expected Value vs the market price.
Signals only fire when:
- Z-score > 1σ from historical mean
- EV is positive vs the market price
- At least 5 games of history exist
Kelly Criterion sizes the bet. 25% hard cap per bet, 15% per team.
Backtested over 30 days: 61% win rate, 7.3% avg EV, 2.1 Sharpe.
Happy to answer questions about the methodology.
u/Dizzy-Maintenance120 — 7 days ago
Built a Z-score model that finds +EV NBA player props on Kalshi
Been playing Kalshi NBA player props for a while and got frustrated trying to manually spot when a line was off. So I built something to do it systematically.
The idea: if a player's rolling 10-game distribution says they hit over X points 65% of the time, but Kalshi's implied probability is only 41%, that's a gap worth betting.
Z-score against each player's last 10 games, blended with empirical frequency, then Expected Value vs the market price.
Signals only fire when:
- Z-score > 1σ from historical mean
- EV is positive vs the market price
- At least 5 games of history exist
Kelly Criterion sizes the bet. 25% hard cap per bet, 15% per team.
Backtested over 30 days: 61% win rate, 7.3% avg EV, 2.1 Sharpe.
Happy to answer questions about the methodology.
u/Dizzy-Maintenance120 — 7 days ago
Built a Z-score model that finds +EV NBA player props on Kalshi
Been playing Kalshi NBA player props for a while and got frustrated trying to manually spot when a line was off. So I built something to do it systematically.
The idea: if a player's rolling 10-game distribution says they hit over X points 65% of the time, but Kalshi's implied probability is only 41%, that's a gap worth betting.
Z-score against each player's last 10 games, blended with empirical frequency, then Expected Value vs the market price.
Signals only fire when:
- Z-score > 1σ from historical mean
- EV is positive vs the market price
- At least 5 games of history exist
Kelly Criterion sizes the bet. 25% hard cap per bet, 15% per team.
Backtested over 30 days: 61% win rate, 7.3% avg EV, 2.1 Sharpe.
Happy to answer questions about the methodology.
u/Dizzy-Maintenance120 — 7 days ago