AI System for Dynamic Odds Pricing (Odds Optimization)

AI System for Dynamic Odds Pricing (Odds Optimization)

Traditional odds setting often lags behind market movements, leading to unbalanced books. We've built an AI system that dynamically adjusts odds for thousands of events in real-time, helping bookmakers increase hold % by 5–8 points and reduce book imbalance below 10%. We have delivered 12 projects for licensed operators. This article covers the key components: probabilistic models, RL agent, and risk management.

How We Build Probabilistic Outcome Models

Base probability is calculated using predictive models: Poisson regression for football (Dixon-Coles), Elo system for combat sports, and Pythagorean expectation for basketball. Inputs include match statistics, lineup news (NLP from Twitter, official sources), and historical form. We use gradient boosting to combine features—CatBoost gave the best quality in our tests.

Market data adjustment is the second step. Sharp money from large players contains insight. The Shin algorithm separates informed betting from noise. We implemented Bayesian update of probabilities when significant bet volumes arrive. This dynamically adapts the line to market activity.

Why RL for Margin Optimization?

Margin is not a constant 5%. An RL agent learns to set different margins based on event liquidity, competitive landscape (parsing Pinnacle, Bet365), and player profile (sharp vs. recreational). We use PPO with a recurrent layer (LSTM) to capture temporal dependencies in bet flow.

The agent's environment includes:

• Current odds on all outcomes
• Incoming bet flow with player profiles
• Book exposure per outcome
• Competitor prices (real-time feed)

PPO is more stable and converges faster than DQN—on a simulator with 5 years of historical data we achieved 30% higher theoretical hold.

Player Segmentation and Risk Management

Each player gets a risk score from 0 (recreational, high limits) to 1 (sharp, limited limits). The score updates on every bet via a Bayesian classifier. Features include profitability, bet timing, and correlation with line movement.

Exposure management:

• Max liability per event: configurable by event category
• Hedge triggering: when 70% limit is exceeded, automatic odds adjustment or hedge purchase on Betfair
• Correlation risk: football matches in the same round correlate; accumulated risk is calculated at the portfolio level

Compare with traditional rules: the RL approach reduces exposure imbalance by 40% compared to rule-based triggers.

How to Manage Odds in Live Mode

Live betting is the most challenging part. Odds must change within milliseconds after goals, red cards, injuries. Our stack ensures end-to-end latency of 3–6 seconds:

We guarantee throughput of 10,000+ odds updates/sec at peak.

How to Deploy an AI System: Step-by-Step Plan

1. Audit current infrastructure—assess integration points and data volume.
2. Collect and prepare data—historical bets, match results, competitor lines.
3. Develop and train models—probabilistic models, RL agent, player classifier.
4. Integrate with platform—connect to your system (SBTech, Kambi, Sportech).
5. Simulator testing—A/B tests on historical data.
6. Deployment and monitoring—phased rollout, monitor metrics via Grafana.

The entire process takes 3 to 10 months depending on complexity.

Performance Metrics

• Hold % (theoretical margin × realized margin): target hold/theoretical > 85%
• Exposure balance ratio: < 15% book imbalance on average
• Lines accuracy: average difference from Pinnacle closing lines < 1%

Timelines: Basic system with pre-match odds and margin optimization—3–4 months. Full in-play with RL and risk management—7–10 months.

With a monthly bet volume of $10 million, a 5% hold increase brings an additional $600,000 per year. The system pays for itself in 6–12 months.

Want to discuss your project? Get a consultation—we will assess opportunities and find the optimal solution for your book. Contact us to get started.

Reinforcement Learning: PPO, SAC, DQN and Industrial Applications

We see projects every day that fail not because of a weak algorithm, but because of incorrect rewards. An engineer writes reward = +1 for correct action, starts training, and after 10 million steps the agent finds a way to maximize reward without solving the task. This is reward hacking — a systemic pain of industrial RL. Our experience shows: proper reward accounts for 70% of success.

Why is RL harder than supervised learning?

In supervised learning, there is a dataset with correct answers. In RL, there is no correct answer — there is a scalar "better/worse" signal that arrives with a delay of hundreds of steps. The agent explores the space and finds a strategy on its own.

Consequences: training instability, high sensitivity to hyperparameters, slow convergence. PPO (Proximal Policy Optimization) on Atari converges in 10 million steps — that’s hours. On robotic tasks with real physics — days or weeks in simulation.

Algorithm selection by task:

How to tune PPO and avoid common problems?

PPO is the workhorse of RL. The main idea: limit policy updates via ratio clipping clip_range=0.2. This provides stability compared to vanilla policy gradient. But without proper tuning, the agent does not converge.

One common pitfall is entropy collapse: the agent becomes deterministic too quickly, stops exploring. Symptom — entropy coefficient drops to zero. Cure — ent_coef=0.01–0.05 and do not lower below 0.001. Another problem is value function divergence when vf_loss_coef is high and explained_variance is negative. We recommend vf_coef=0.5 and gradient clipping max_grad_norm=0.5.

Incorrect n_steps also breaks training. n_steps=2048 is Stable-Baselines3 default. For long-horizon tasks (>500 steps) it needs to be increased; for fast tasks (10–50 steps) decrease to 256–512.

For quick start, use stable-baselines3 + sb3-contrib. For research and custom algorithms — tianshou or CleanRL.

SAC for continuous control

SAC (Soft Actor-Critic) adds entropy maximization to the objective — the agent learns to be both efficient and diverse. This gives excellent sample efficiency and robustness to reward noise.

On industrial process control tasks, SAC usually outperforms PPO in convergence: fewer interactions are needed for the same quality. The key parameter is target_entropy. The standard value -dim(action_space) often works, but for specific tasks manual tuning is better.

How to transfer a trained agent to a real device?

Training RL on a real robot is expensive and dangerous. Standard approach: train in simulation → transfer to real hardware. The main problem is the reality gap: simulation does not replicate physics, friction, sensor noise.

The primary tool is domain randomization. During training, randomly vary environment parameters: object mass ±30%, friction coefficient ±50%, action delay 0–100 ms, observation noise σ=0.01–0.1. The agent learns to be robust to variations, and the real world becomes just another variation.

Comparison of popular simulators:

RLHF: training LLMs from human feedback

RLHF became the standard after InstructGPT. Classic scheme: supervised fine-tuning → reward model → PPO.

Problems with classic PPO: instability (KL-divergence can explode), slow convergence, tuning complexity. Hence popular alternatives:

• DPO — bypasses reward model, learns from preference pairs. Simpler, more stable, but less flexible.
• GRPO — used in DeepSeek-R1, good for reasoning tasks.
• ORPO — combines SFT and alignment into one stage.

The trl library from Hugging Face is the standard. Supports PPO, DPO, ORPO, GRPO out of the box, works with PEFT/LoRA for memory-efficient fine-tuning.

"Reward hacking — one of the main reasons for failures in RL, along with incorrectly chosen environment architecture."

What is included in the work

• Architectural solution and justification of algorithm selection
• Development and documentation of the reward function
• Creating a simulator or configuring an existing one
• Training, hyperparameter sweep (Optuna / Ray Tune)
• Transfer to real hardware or integration into product
• Documentation, access to code and simulators
• Team training and 3-month support after deployment

Work process

1. Task audit — define goals, resources, constraints.
2. Reward engineering — formalize desired behavior, check for reward hacking.
3. Environment and algorithm selection — baseline, first runs.
4. Systematic hyperparameter sweep — use Optuna.
5. Training in simulation with domain randomization.
6. Testing on real equipment (if necessary).
7. Deployment, monitoring, support.

Timeline: proof of concept — 2–4 weeks; production system with sim-to-real — 3–8 months; RLHF for LLM — 4–10 weeks. Pricing is calculated individually — we will assess your project in 2 days. Contact us for a consultation.

Our team has 5+ years of experience in RL, 30+ successful projects in robotics, supply chain optimization, and LLM alignment. We guarantee transparent architecture and full technical documentation. Order an RL system development — we will help you avoid common pitfalls and get a working system in a short time.