Methodology

1. Data Pipeline Overview

CS2Bet.io processes esports data through a multi-stage pipeline designed for accuracy and freshness:

1. Data ingestion: Raw match data, player statistics, team rosters and tournament structures are pulled from the PandaScore API — a licensed esports data provider. Live matches sync every 30 seconds. Structural data (leagues, series, tournaments) syncs twice daily. Team and player career stats sync daily.
2. Validation: Incoming data passes automated quality checks: type validation, range plausibility (e.g., ratings between 0 and 3, kill counts non-negative), duplicate detection and cross-entity consistency (a player's team ID must correspond to a team that exists in our database).
3. Storage: Validated data is stored in a structured relational database with full historical retention. No data is overwritten — changes create new records, allowing us to track trends and roster changes over time.
4. Enrichment: Raw statistics are enriched with computed metrics: career averages, per-map breakdowns, form ratings (weighted recent performance), head-to-head records and map pool overlaps.
5. Presentation: Enriched data is served on entity pages (teams, players, matches, tournaments) with dynamic narratives that contextualise the numbers in natural language.

2. Match Prediction Model

Our prediction model combines multiple statistical signals to generate probabilistic match outcomes. Here is how it works:

2.1 Input Features

For each upcoming match, the model evaluates:

• Historical head-to-head record: Win/loss ratio between the two teams across all tracked matches.
• Recent form (weighted): Performance in the last 10-20 matches, with more recent results weighted more heavily. A 14-day exponential decay function reduces the influence of older matches.
• Player-level statistics: Individual HLTV 2.0 ratings, ADR, KAST%, K/D ratios and headshot percentages for each player in the expected lineup.
• Map pool overlap: Comparing each team's map win rates on the maps most likely to be played, based on recent veto patterns.
• Tournament context: Whether the match is a group stage, playoff, grand final or online qualifier — each format has different pressure dynamics that affect performance.
• Roster stability: Recent roster changes are penalised, as newly formed lineups typically underperform their individual statistics.
• Fatigue factor: Teams playing their third or fourth match of the day may receive a slight downward adjustment.

2.2 Model Architecture

The prediction engine uses an ensemble approach: a gradient-boosted decision tree (XGBoost) trained on historical match outcomes is combined with an Elo-based rating system. The ensemble is calibrated so that a "65% confidence" prediction historically resolves correctly approximately 65% of the time (Brier score calibration).

2.3 What the Model Does NOT Do

• It does not factor in insider information, rumours or unconfirmed roster changes.
• It does not consider betting odds from sportsbooks — predictions are generated before odds are checked, to prevent circular reasoning.
• It does not guarantee outcomes. A 75% prediction means the model expects the favoured team to lose 1 in 4 times.

3. Accuracy Tracking

We publicly track our prediction accuracy because accountability matters. Current lifetime accuracy: publicly tracked across hundreds of predictions.

Accuracy is measured as:

• Win rate: The percentage of matches where the predicted winner was correct.
• Calibration: How closely predicted confidence percentages match actual outcomes (e.g., predictions at 70% confidence should be correct roughly 70% of the time).
• ROI (vs closing odds): Whether following our predictions would yield a positive return if flat-staked against bookmaker closing lines.

Accuracy statistics are updated daily and displayed on the predictions page. We do not retroactively edit, hide or remove incorrect predictions. Every prediction ever published remains visible in our archive.

4. Team and Player Statistics

All statistics displayed on team and player pages are computed from raw match data:

4.1 Player Statistics

• HLTV 2.0 Rating: Sourced from PandaScore, calculated using the HLTV 2.0 methodology that accounts for kills, deaths, rounds survived, multi-kill rounds, opening kills, support damage and trade success.
• K/D Ratio: Total kills divided by total deaths across all professional maps tracked.
• ADR (Average Damage per Round): Total damage dealt divided by total rounds played.
• KAST%: Percentage of rounds in which the player recorded a Kill, Assist, Survived, or was Traded.
• Headshot %: Percentage of kills that were headshots.
• Per-map breakdowns: All of the above, split by competitive map for map pool analysis.

4.2 Team Statistics

• Win rate: Total match wins divided by total matches played.
• Map win rates: Win percentage per map in the active competitive pool.
• Average player rating: Mean of all rostered players' HLTV ratings across recent matches.
• Round win rate: Total rounds won divided by total rounds played (indicates round-level competitiveness).
• Form rating: A weighted recent-match performance score with exponential decay — more recent matches count more.

5. Ranking Methodology

Our internal team rankings are based on a composite score combining:

1. Match results (40%): Win/loss record weighted by opponent strength (beating a top-10 team is worth more than beating a rank-200 team).
2. Recent form (30%): Performance in the last 30 days, with a 14-day exponential decay.
3. Tournament performance (20%): Placement in recent tournaments, weighted by tournament tier (S-tier events count more than qualifiers).
4. Roster stability (10%): Teams with recent roster changes receive a temporary penalty until 10+ matches with the new lineup are recorded.

Rankings update daily. Our ranking is independent of HLTV, ESL or Valve rankings and may differ from those systems. We provide our ranking as an additional data point, not a replacement.

6. Odds Comparison

Betting odds displayed on CS2Bet.io are sourced in real-time from licensed sportsbook APIs. We display odds as-is without manipulation. Odds comparison features show the best available price across our partner operators to help users identify value.

We do not set odds, operate a sportsbook or act as a bookmaker in any capacity. For our affiliate relationship with these operators, see our Editorial Policy.

7. Limitations and Disclaimers

• Our prediction model is statistical. It cannot account for every variable — player health, team morale, in-game bugs or last-minute stand-in substitutions may not be captured.
• Historical data may be incomplete for newer teams or players with limited professional track records. Pages for entities with insufficient data are clearly marked.
• API data depends on our upstream provider (PandaScore). Temporary delays or errors in their feed can briefly affect the accuracy of displayed statistics.
• No prediction or analysis on this site constitutes financial or betting advice. Always gamble responsibly.

8. Open Questions? Contact Us

If you have questions about our methodology, want to report a data discrepancy, or are a researcher interested in our data pipeline, please reach out:

Email: data@cs2bet.io
Or visit our contact page.