The Anti-Cheat Cheat Sheet
CS2 is a game of controlled chaos. Five players, thirty rounds, and a single wrong peek can flip a series. Our model cuts through the noise by combining five independent signals into a calibrated win probability, then only surfaces picks where the math decisively favors one side over the market price.
The Challenge: Fragile Margins in a Volatile Game
In traditional sports, the better team wins most of the time. In CS2, the better team can lose any single map to a pistol round snowball, an eco-round ace, or a clutch defuse in overtime. A Best-of-1 is essentially a coin flip with a thumb on the scale. Even in a Bo3, the underdog wins roughly one in three series against a meaningfully stronger opponent.
Round Economy
CS2 maps are won round-by-round, and losing a single pistol round can cascade into a 4-round deficit. One clutch play can swing an entire half's economy.
Map Variance
Each map plays like a different game. A team dominant on Inferno can be mediocre on Nuke. The veto phase alone can shift win probability by double digits.
Online vs. LAN
Teams that dominate online qualifiers can crumble on LAN under crowd pressure. Others rise to the occasion. The venue changes the game.
The opportunity: Because esports lines are set by thinner markets with less sharp action than NFL or NBA, mispricings persist longer. A model that quantifies the right dimensions of team strength, and knows when its own data is reliable enough to act, can find edges that survive the juice.
The Five-Signal Pipeline
How the Model Thinks
The model operates like a head scout, evaluating a match across five independent dimensions. No single dimension tells the full story, but together they build a composite picture of which team has the structural advantage.
Each signal is normalized to a common scale, weighted by its predictive importance, and combined into a single differential score. That score is transformed into a probability between 0% and 100% through a proprietary function that acts as a confidence dial: small advantages produce probabilities close to 50/50, while large advantages push the needle toward certainty, but it never reaches 100%.
This architecture ensures that the model is self-limiting. Even if every signal agrees, the output is bounded. No team is ever rated as a sure thing.
Signal 1: Ranking Differential
The dominant factor. Official rankings capture the cumulative results of hundreds of official matches. The model normalizes the gap between two teams' positions to produce a relative strength score.
Signal 2: Recent Form
Rankings tell you where a team has been. Form tells you where they are right now. The model evaluates recent performance to capture momentum and trajectory.
Signal 3: Head-to-Head Record
Some matchups have a psychological or tactical dimension that pure rankings miss. If Team A has beaten Team B in four of their last five meetings, that pattern carries predictive weight, but only when there's enough history to trust it.
Signal 4: Tournament Context
Not all tournaments are created equal. Higher-stakes events produce more reliable signals because teams prepare harder and play closer to their true ceiling. The model adjusts its confidence based on the competitive context of each match.
Signal 5: Venue, Format & Scheduling
Three contextual factors applied after the base probability is computed. Venue type, match format, and scheduling dynamics all influence expected outcomes. The model adjusts for these situational variables to refine its probability estimates.
Zeroing the Scope: Calibration
Why Raw Probabilities Need Correction
A raw model is like a rifle that shoots accurately but pulls slightly left. The grouping is tight, the relative aim is correct, but every shot lands a few inches off-center. Calibration is the process of zeroing that scope.
After running the model through hundreds of historical matches, we measure the gap between what the model predicted and what actually happened in each probability range. If the model says 65% win probability but the actual win rate in that bucket is only 58%, the calibration layer applies a correction curve that pulls future 65% outputs down to where they belong.
Crucially, calibration preserves the model's ranking of matches. A match the model rates as 70% will still be rated higher than one at 60%. Only the absolute values shift to align with observed reality.
The calibration table is re-fitted as more graded picks accumulate, ensuring the correction evolves with the model's performance over time.
The Quality Gates
Computing a probability is only the first step. Before a pick reaches you, it must survive a gauntlet of filters designed to reject anything where the edge is uncertain, the data is thin, or the risk-reward profile is unfavorable.
Gate 1: Edge Threshold
The model probability must exceed the de-vigged fair market probability by a minimum margin. Thin edges get eaten by the juice and variance. Only meaningful mispricings pass.
Gate 2: Expected Value
Every pick must have positive mathematical expectation above a floor. If the edge is real but the payout structure doesn't compensate for the risk, the pick is rejected.
Gate 3: Odds Range
Heavy favorites and long underdogs are excluded. The model focuses on a competitive odds window where pricing is most likely to contain exploitable inefficiencies.
Gate 4: Data Quality
Both teams must have Valve rankings and sufficient recent match history. Picks with thin data are either filtered entirely or downgraded in confidence tier.
Confidence Tiers & Unit Sizing
Picks that survive all four gates are assigned a confidence tier based on edge strength and expected value. Higher tiers receive larger unit allocations, reflecting the model's conviction level.
Max Confidence
The strongest edges. Both edge and EV clear the highest thresholds. These are the model's highest-conviction plays.
Strong Confidence
Clear edge with solid expected value. Reliable plays that form the backbone of consistent performance.
Medium Confidence
Meets all minimum thresholds with a real but smaller edge. Smaller unit size reflects the tighter margin.
Technical Breakdown
Data Pipeline
| Component | Description |
|---|---|
| Team Rankings | Official rankings synced regularly, normalized against the active competitive landscape |
| Match History | Recent results, head-to-head records, and form calculations from finished matches across tracked tournaments |
| Tournament Metadata | Tier classification (S/A), prize pool, LAN vs online, and event location for context adjustments |
| Odds Feed | Pinnacle moneyline odds fetched every 2 hours for de-vigging and edge calculation |
| Map Pool Data | Team-level map performance data and strategic tendencies |
Probability Engine
| Step | Process |
|---|---|
| 1. Feature Extraction | Pull ranking differential, recent form, H2H record, map pool advantage, tournament tier, format, venue, and rest days |
| 2. Differential Score | Weight and sum the primary signals (ranking, form, H2H) into a single composite advantage score |
| 3. Tournament Multiplier | Adjust confidence based on the competitive tier and stakes of the event |
| 4. Probability Transform | Convert the scaled score into a base probability (0-100%) using a proprietary function |
| 5. Context Adjustments | Apply situational corrections for venue type, match format, and scheduling dynamics |
| 6. Guardrails | Enforce probability bounds to prevent extreme outputs in closely matched or mismatched scenarios |
| 7. Calibration | Proprietary calibration layer to align outputs with historically observed outcomes |
| 8. Edge Calculation | De-vig the market line, compute fair probability, and measure the gap between model and market |
| 9. EV & Filtering | Calculate expected value at current odds, apply all quality gates, assign confidence tier |
| 10. Data Quality Check | Score data completeness (rankings, form depth, H2H history), filter or downgrade picks with thin data |
Operational Schedule (UTC)
| Time | Job |
|---|---|
| 6:00 AM | Sync teams and tournament metadata from data provider |
| Every 4 hours | Sync match results and update form calculations |
| Every 2 hours | Fetch latest Pinnacle odds for upcoming matches |
| 30 min after sync | Run projection engine: extract features, compute probabilities, evaluate picks |
| Every 15 min | Check for picks approaching lock window (8 hours before match start) |
| 7:00 AM Monday | Weekly Valve ranking sync to refresh ranking differential baseline |
| 8:30 AM | Daily settlement: grade yesterday's locked picks against verified results |
How We Measure Success
Closing Line Value (CLV)
CLV measures how the market moves after we lock a pick. If we take a team at +130 and it closes at +110, the market confirmed our read. Consistently beating the closing line is the strongest predictor of long-term profitability, especially in esports where line movement can be dramatic.
Every locked pick is tracked against its closing line. This metric is reported alongside win rate and ROI for full transparency.
Tiered Unit Sizing
Rather than flat-betting every pick, the model allocates units proportional to conviction. Max-confidence picks receive the largest allocation, while medium-confidence plays get smaller sizing. This ensures the bankroll is concentrated where the edge is widest.
Unit sizing is determined entirely by the model's confidence tier. There is no manual override or subjective adjustment.
Automated Settlement
Results are settled automatically each morning using verified final scores. Every pick has a clear paper trail: model probability, odds at lock time, closing odds, actual result, and profit/loss. No manual intervention, no cherry-picking.
Continuous Recalibration
As more picks are graded, the calibration table is periodically re-fitted to keep the model's absolute probabilities aligned with observed outcomes. The ranking signal adapts too: the dynamic pool size adjusts to reflect the current competitive landscape.
The Bottom Line
The CS2 Moneyline model is built on three principles: quantify advantage across multiple dimensions, calibrate probabilities against reality, and only bet when the edge decisively clears the noise.
- We combine five independent signals: rankings, form, head-to-head, tournament context, and venue/format conditions
- We transform raw advantage into calibrated probabilities using a sigmoid function corrected by empirical data
- We enforce strict quality gates: edge threshold, expected value, odds range, and data completeness
- We assign confidence tiers with proportional unit sizing, concentrating capital where conviction is highest
- We require both teams to be Valve-ranked with sufficient match history before generating any pick
- We surface fewer picks with higher conviction, not more picks with thinner edges
Monthly Access
- Predictions only go live when the model finds true edge
- Closing line value tracked on every prediction so you can verify it yourself
- Covers every market we model and we're always adding more
- Cheaper than your average unit size
Annual Access
- Get 4 months free on us when you go annual
- Every new model we ship is included automatically
- Full platform access for less than most services charge monthly
- Models run 365 days, your subscription should too