How bookmakers use neural networks for forecasts

Introduction: why AI has become the "engine" of the line

The modern line is not only the expert opinion of the trader. This is a chain of models: forecast of outcomes/totals → calibration → price, taking into account margins and limits → market monitoring. Neural networks accelerate and deepen each layer, especially in live and complex prop markets (players, quarters, maps/rounds in cyber).

1) Data: from which the forecast is "boiled"

Structural: results, lineups, minutes, play-buy-play, tracking (x, y coordinates, speed, pressure), in cyber - peak/ban, economic cycles (CS2), objects (Baron/Roshan).

Context: schedule, fatigue/flights, referees, weather, coverage, game patches, BO1/BO5 format.

Transactional: customer rates, market movement, closing prices of "reference" books.

Non-structural: video (CV models for tracking), text (NLP on news/insights, social signals).

Meta-features: "league strength," price elasticity, "stickiness" of totals to key events.

2) Model architectures (no water)

Sequences: LSTM/GRU/Temporal Convs/Transformers (time dependencies, live log of events).

Graph networks (GNN): igrok↔komanda connections, transfers, peaks/synergies in MOBA.

Multimodal transformers: combine tabular features, text and visual.

Gradient boosting as a backbone: for stable prematch markets, often in an ensemble with NN.

Bayes/quantile models: confidence intervals, range prediction.

RL/control: recommendations on limits/margins, dynamic cache-out (not "guessing the score," but optimization of profit/risk).

3) Probability to Coefficient: The Kitchen of Pricing

1. Prognosis p (event) →

2. Calibration (Platt/Isotonic, temperature scaling) and regularization to "closing" (so as not to "get crazy" from noise) →

3. Margin (round) + skids for correlations (SGP/Bet Builder) →

4. Limits and exposure (market/customer threshold) →

5. Publication and auto-restructuring during events (goal, removal, pistol).

The key: not only "how likely" but "at what price it is safe to sell," given the risk appetite and liquidity.

4) Live simulation: reactions in milliseconds

Event flow (Kafka/PubSub) → features in real-time (tempo, pressing leverage, PVP duels, round economics) → seq2seq/temporal transformer gives updated p-scores.

Triggers: goal/trickle/red/timeout/pistol - recalculation of totals/handicap, rearrangement "race to N."

Cash out: RL policies + price elasticity → partial fix offer.

5) Prop markets and SGP: where the neural network is especially strong

Game props: minutes/usage → points/assists/ribounds; in kibera - killies/damage/objects by role.

Correlations for SGP: co-variations of players within a match; penalization so as not to underestimate the overall margin.

Single-game simulations: Monte Carlo based on NN projections gives distributions, not just the median.

6) NLP and CV in rates

NLP: Transformers "understand" news/tweets/line-up releases; detect injuries, rests, patch notes.

Computer Vision: tracking x, y and events (xG/xThreat), positional error estimation.

Multimodality: merge table + text + video → more resistant to data omissions.

7) Quality: how to check that the model "is not lucky"

Backtest/forward-test: sliding window, walk-forward; CRPS/LogLoss/Brier, AUC-PR for rare events.

Calibration plots/Reliability diagram: equality of probabilities and frequencies.

CLV metric: shift to closing line - practical indicator.

AB pricing tests: control/test on parts of markets/regions.

Stress tests: in-game patch, ball/surface change, abnormal weather windows.

8) Drift, sabotage and defence

Concept drift: monitoring distributions, alerts for shifting features, quick retraining.

Anti-adversariality: protection against "signal" attacks (mass bet in thin markets), rate limits, abnormal client traffic.

Model "sanitation": versioning, feature store, lineage, reproducibility, canary-depla.

9) Human-in-the-loop: where you can't go without a trader

Thin leagues/exotic: little data is the priority of expert feedback edits.

Incidents: warm-up injuries, massive colds, force majeure, DDoS feeds.

Markets with social sensitivity: manual limits and additional checks.

10) Ethics, compliance and red lines

Transparency of rules: how overtime/transfer/void is interpreted.

Responsible game: offers are personalized, but do not manipulate vulnerable segments; limits - default.

Bias control: Models should not fine groups of players/leagues due to noisy data.

KYC/AML: AI helps weed out moolish patterns, but interlock solutions are human-driven.

11) Mini Case: Football, Basketball, CS2

Football: transformer for play-by-play + weather/referee → total/both will score; CV-xG improves response to "long attacks."

Basketball: tempo model + substitutions/fouls → minute projections usage; calibration of props "points + rebounds + gears."

CS2: GNN on map pool and roles + seq-model economy rounds → "total rounds," live on "pistol/force/retake."

12) MLOps bookmaker stack (scheme in words)

Raw feeds → ETL/fichestore → training (GPU/once a day + online updates) → model register → inference service (low latency) → pricing/margin → monitoring (latency, quality, drift) → feedback from customer rates → a new iteration.

13) Typical mistakes and how to avoid them

1. Races for RMSE uncalibrated. The result is beautiful numbers, bad odds.

2. Forgotten correlation penalty in SGP. Underestimating the risk of combined expresses.

3. Single "universal" pricing for all leagues. Hierarchical/league-specific layers are needed.

4. There is no stress plan for patches/play-ins. Keep the "switches" and manual modes.

5. Opacity for support. Mandatory - audit-trail and explainable features (SHAP/ICE).

14) Checklists

For product/data

Is there tracking data or just an account?

Fichestor online + offline synchronized?

Is the closing price connected as an anchor?

Monitor calibration and CLV by segment?

For pricing

Are correlations considered in SGP/pairs?

Are league limits/exposures set up?

Are there RL cache out policies?

Inference delay threshold ≤ feed delay?

For liability and compliance

Are limits and timeouts enabled by default?

Are line edits and justifications logged?

Blocking decisions - involving humans?

Neural networks do not "guess the future," they structure uncertainty and turn it into a manageable price. The best operators combine multimodal models, rigorous calibration, MLOps discipline and human expertise. Bottom line - lines that react faster, make mistakes less often and are more honestly explained. And for the player, this means a more stable "probability price" and less "magic" - more understandable rules of the game.