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How to predict athletic performance with data
Prediction in sports is not a "guess," but a systemic assessment of probabilities. It is important not to predict the exact score, but to buy the correct price for the outcome with a certain uncertainty. Below is a step-by-step process: from collecting data and building features to calibration and combat operation.
1) Data: model foundation
Sources
Match: lineups, injuries, disqualifications, schedule (b2b/flights), home/away status, weather/surface/arena, referees.
Tracking/game events: play-by-play, coordinates, events (corners, fouls, throws, passes).
Advanced metrics: xG/xA (football), eFG %/pace/ORB (basketball), DVOA (American football), bullpen/park factors (baseball), map pool/patches (esports).
Market: movement of lines that close coefficients (CL), amounts of money - useful for marking the "reference" probability.
Team/Player Stories: Last Matches Form N, Style H2H, Minutes/Load Model.
Quality
Synchronize time zones and clock types (event time vs processing time).
Remove duplicates, fill in gaps with documented rules.
Fix the sources of "truth" for the final statistics (for example, what is considered official xG/strike).
2) We formulate the problem
Types of targets
Classification: win/draw/loss; "both will score"; whether there will be a tiebreaker.
Score/intensity: expected goals/points (Poisson/negative binomial).
Distribution forecast: totals, individual indicators (CRPS as quality metric).
Player props: points/assists/aces/yards - regression with hierarchical (mixed) effects.
Horizon
Prematch (T-minutes to start).
Live (during the event) - Adds streaming features and delay limits.
3) Feechee: What really explains the outcome
Team level
Strength (Elo/PRI), offensive/defensive quality difference.
Tempo (pace), style (pressing/low block; 3PT rate; rush/pass mix).
Form and "fatigue" (minutes/load, b2b, travel).
Special teams: PP/PK in hockey, special teams in American football.
Player level
Minutes/participation model, role (usage), effectiveness (eFG%, OBP, xwOBA).
Compositions: the effect of specific combinations of fives/links.
Context
Weather/surface/arena, referee profile (foul/penalty).
Tournament motivation (survival, playoffs, rotation before European competitions).
Market
Lines/totals/odds, spreads between operators, movement to closure (proxy information).
4) Models: from classics to neural networks
Classification/probabilities
Logistic regression (baseline calibrated benchmark).
Gradient boosting (XGBoost/CatBoost/LightGBM) is a strong tabular standard.
Neural networks (MLP) - with a large number of nonlinearities and interactions.
Score/intensity
Poisson/two-dimensional Poisson (football, handball).
Negative binomial (overdispersion).
Hierarchical models for players/teams (partial pooling).
Sequences/live
RNN/GRU/Temporal CNN and transformers for play-by-play, momentum and tempo changes.
Bayesian real-time intensity updates.
Ratings
Elo/Glicko dynamically reflect strength; can be combined with stacking.
5) Calibration and interpretability
Why calibrate? The probabilities must coincide with the actual frequencies.
Platt/Isotonic/Beta calibration over raw predictions.
Calibration diagrams, Brier score, LogLoss - basic metrics.
Interpretability: permutation importance/SHAP to control shifts and common sense.
6) Honest validation: without it, everything else is meaningless
Walk-forward (sliding window)
Divide by time: train → validate → test. No shuffling into the past.
At least 3-5 "rentals" of the window to understand stability.
Preventing leaks
Do not use post-invoice characteristics (the final xG of the match when predicting the start of the match).
In live - features are available only until the current time.
Separate "before the announcement of the compositions" and "after": these are different modes.
Metrics
Probabilities: Brier/LogLoss + calibration.
Regressions: MAE/RMSE/CRPS.
Business metrics: hit-rate by price thresholds, stability on league/season cohorts.
7) Probability to Decision: Price and Strategy
Clear margin (around)
In the 1X2 market, the sum of "dirty" probabilities is> 100%. Normalize proportionally to get "honest" (p ^ {fair}).
Value и EV
Edge: (\text {edge} = p\cdot d - 1).
Set only if the edge ≥ the threshold (for example, 3-5%).
Bet size
Flat 0. 5-1% for singles; less - on express trains.
Kelly's fraction: (f =\frac {p d - 1} {d - 1}), more often used ¼ - ½ Kelly due to variance and errors (p).
CLV as quality criterion
Compare your price with the closing price. Long-term + CLV is a sign of a healthy pattern and timing.
8) Live forecasting: speed and "windows"
Pipeline
Event → update feature → online inference → risk check → publication.
Delay targets: inference <0. 8s, update cycle 0. 5-2 s.
Real-time features
Tempo/ownership, fouls/cards, fatigue, special teams, economic cycles in esports.
Suspension modes at "sharp" moments; models should be able to "fall silent."
Practice
Look for "overheating" lines immediately after micro events (10-0 jerk, early break), but take into account the stream delay - buy logic, not a picture.
9) Mini-cases by sport
Football (totals/outcomes)
Fici: xG for 8-12 matches (weighted), pace and style of pairs, referee (penalty/cards), rotations.
Model: two-dimensional Poisson with home factor + calibration.
Conclusion: the forecast of the distribution of goals → the price of totals/Asian lines.
Basketball (totals/props)
Features: pace, eFG%, ORB/DRB, fouls/bonus, minute routine.
Model: boosting for total; for props - hierarchical regression of minutes × efficiency.
Conclusion: probability of total zones, medians/quantiles for players' points.
Tennis (exodus/games)
Features: coverage, hold/break%, second serve quality, fatigue.
Model: Markov in points/games + logistics "layer" in shape; calibration.
Conclusion: probability of victory/tie-break, totals of games, live updates for each serve.
Esports (Maps/Rounds)
Features: pool card, ban/peak, economic cycles, LAN fatigue, patches.
Model: boosting/transformer by event; for cards - classification + CRPS for rounds.
Conclusion: card winner, round totals, "first blood/object."
10) MLOps and operation (advanced)
Fichstore: offline/online consistency, time travel for honest backtests.
Data/model versioning, CI/CD, canary releases.
Monitoring: data drift, calibration degradation, inference latency.
Experiments: A/B without SRM, CUPED/diff-in-diff, pre-prescribed stop criteria.
Fail-safe: fallback lines and manual rules for feed incidents.
11) Bugs and anti-patterns
Leaks: signs from the future, post-fact metrics in prematch.
Retraining: too complex model on a small dataset; is solved by regularization, checking for time.
Recency bias: reassessment of recent matches; use exponential weights with maximum constraint.
Anchoring: snapping to first line; compare with the "honest" price of the model.
Calibration ignore: An "exact" model with curved probabilities breaks the EV.
Mixing modes: "before compositions" and "after" - different models.
12) Checklists
Before training
1. Data is cleared and synchronized in time.
2. Target statement: what we predict and why (what decision we will make).
3. Split train/valid/test time only.
4. Base benchmark model (logistic/Poisson).
Before publication
1. Calibration verified (Brier/LogLoss, reliability plot).
2. Walk-forward is stable on seasons/leagues.
3. There are no leaks, features are available in prod.
4. There is monitoring of drift and overtraining.
Before Bet
1. Margin removed, edge ≥ threshold.
2. Flat/Kelly share rate.
3. Quality Assessment Plan - CLV tracking.
4. Understanding calculation rules (OT/VAR/push/void).
13) Ethics and responsibility
Models are a tool, not a "money button." Respect time/money limits, pause, do not use insider/dishonest sources, and remember that even the perfect model is wrong on individual matches. Your goal is a distance advantage, not a "100% hit."
Predicting sports performance with data is a cycle: data → features → model → calibration → honest validation → price decision → post-analysis. Do not chase exotic: a slender benchmark, clean data and calibrated probabilities are often stronger than "fashionable" architectures. Add complexity only when it gives a steady increase in quality on the walk-forward and improves CLV. Do less, but better - and the distance will start working for you.