As we approach the later stages of the season, it’s the perfect time to review the model’s performance, share key learnings, and discuss the best path forward.

1. Model Degradation Ahead

Historically, model accuracy tends to decline sharply after Match #45 — and we are entering that zone now.

There are several reasons why predictive performance drops:

• Top teams start resting key players.

• Injuries become more common.

• Bottom teams begin experimenting with new lineups and playing combinations.
  

These changes are often announced only an hour before the match, making it much harder to accurately predict outcomes. The volatility increases, and with it, the risk.

Recommendation: If I were advising a hedge fund today, my strong recommendation would be to stop betting and lock in profits. Risk-adjusted returns are unlikely to improve from here. That said, I will continue to send picks for those who wish to stay active for the remainder of the season.

2. Current Performance Snapshot

Despite the caution ahead, the algorithm's performance so far this season has been outstanding.

Return on Investment (ROI):

• 🥇 Conservative: 126% ROI

• 🥈 Moderate: 170% ROI

• 🥉 Aggressive: 188% ROI
  

Model Accuracy:

• Overall model accuracy: 72%

• Betting strategy accuracy: 61%
  

For context, typical betting agency models operate around 54–56% accuracy, and professional betting funds usually range between 57–62%. Our models have comfortably outperformed both benchmarks.

The graph below shows the evolution of ROI and model accuracy across the season. After a slow start, accuracy is now trending at 72%, the highest we’ve recorded — previous seasons typically hovered around 65–68%.

3. Key Learnings from This Season

a) Dynamic Model Selection ("Starting XI") Pays Off

Actively promoting and dropping models — much like rotating players in a team — has meaningfully improved accuracy throughout the season. Flexibility and dynamic selection are critical.

b) Weight Model Inputs Differently as Season Progresses

Early in the season, it made sense to rely more heavily on the betting strategy model. As the season advanced and the models gathered more data, it became smarter to lean on the pure "Who will win" predictions. If we had solely followed the "Who will win" model throughout, the returns would have been even stronger:

• Conservative: 182% ROI

• Moderate: 220% ROI

• Aggressive: 240% ROI
  

The difference comes from the compounding effect of small wins.When betting on the model’s direct prediction — especially in Matches #25, #33, #38, and #45 — small victories added up, boosting the bankroll over time.This highlights that once enough data is collected, trusting the raw model output yields better long-term outcomes.

c) Wait for More Data Before Placing First Bets

Currently, predictions and bets start once every team has played one game. However, if we had waited until each team had played three games (~15 games into the season), accuracy would have risen to 78% (21 wins out of 27 bets), compared to the current 72%.

Takeaway: Patience early on — letting the models learn from a few games — materially boosts prediction quality.

Final Thoughts

Our cricket betting models have delivered outstanding results this season, consistently outperforming industry benchmarks across major cricket prediction markets. That said, with model degradation now evident, strategic caution is key. Locking in profits helps preserve capital for future, higher-confidence opportunities. We’ll continue sharing picks for those staying active - just note that volatility and risk are elevated as we enter the late-season phase. More refinements ahead! 🚀