A full research platform for NBA player-prop betting: a weighted-average prediction model, an expected-value parlay analyzer, a paper-trading workflow, and a browser extension. Built to explore why predicting performance is not enough to establish a betting edge.
The question
A prediction is only half the bet.
This project explores the full path from NBA game logs to player-stat predictions, probabilities, and expected value. The engineering challenge was connecting a data pipeline, prediction engine, API, and paper-trading workflow into one research platform.
The mathematical takeaway: predicting performance is not enough to beat the payout structure. Without reliable probability estimates and an evaluation against actual offered prices, an apparent edge is just an assumption.
From past games to a probability.
A recent game counts more than an old one.
A weighted average gives recent observations more influence. With exponential decay, the weight shrinks as a game gets older. Context adjustments for opponent, rest, and home/away try to account for differences that a simple average misses.
μ̂ = Σ(wᵢ × xᵢ) / Σwᵢ, with wᵢ = exp(−λ × ageᵢ)
Here xᵢ is a past stat, λ controls how quickly history fades, and μ̂ is the estimated mean. Faster decay adapts quickly but can overreact to a short streak.
An average is not a chance of winning.
The model uses a normal-distribution approximation to translate an estimated mean and spread into a probability of exceeding a line L. Φ is the standard normal cumulative distribution function.
P(X > L) ≈ 1 − Φ((L − μ̂) / σ̂)
That approximation is a modeling choice, not a law of basketball. Stats can be discrete or skewed, playing time changes, and injuries can shift the distribution. Integer lines also require accounting for ties or pushes under the relevant rules.
The payout sets the hurdle.
At −110 odds, a $110 stake wins $100 in net profit. Expected net is 100p − 110(1 − p), so break-even is 110 / 210 ≈ 52.38%. A true 50% chance has negative expected value at that price.
EV = p × profit if won − (1 − p) × stake
Evidence & limitations
Built as a research tool. No profitability claim.
I do not have a documented win rate, ROI, or completed backtest to report. The findings above are mathematical examples and modeling limitations, not measured betting returns. The paper-trading workflow provides infrastructure for evaluation; building it does not itself validate the model.
A next evaluation would freeze the model, log predictions against timestamped lines, and measure calibration and net returns on future games, including uncertainty. Until then, the work demonstrates software engineering and probabilistic reasoning—not a proven betting strategy.