Value betting exists only where pricing errors persist. Large historical datasets from bookmaker feeds and exchange markets show that these errors appear more often in specific sports, leagues, and market structures. Platforms such as 1xbetcambodia.com provide datasets that include opening odds, price updates, and historical records across large event samples, which supports long-term market observation.
Odds behaviour follows statistical patterns. These patterns are measurable. They repeat across seasons. They do not rely on opinion. They appear inside datasets that track opening price, closing price, liquidity depth, and margin size.
Market efficiency differs sharply between sports
Efficiency measures how fast odds move toward true probability. High-efficiency markets correct fast. Low-efficiency markets remain distorted longer.
Exchange datasets covering over 2.1 million events show that elite football leagues correct 70–85% of early mispricing before kickoff. Non-elite football corrects only 48–62%. Tennis main-tour events correct about 75–82%. Challenger-level tennis corrects only 52–60%.
Liquidity explains much of this. Elite football matches often exceed €5–20 million in combined market volume. Lower-tier football rarely exceeds €300,000. Smaller markets absorb information slower.
Three structural factors explain most inefficiency differences.
- Market liquidity in the first hour after opening
- Public bias toward popular teams or athletes
- Bookmaker margin size across the market
These factors appear consistently across multi-season datasets.
Sports where mispricing persists longer
Historical price movement shows that some sports produce larger and more durable deviations between early odds and fair probability.
Lower-tier football leagues produce the largest repeatable deviations. A multi-season sample of 42,000 matches showed opening odds deviated by more than 7% from closing consensus in 23.4% of matches. Elite competitions showed such deviation in only 9.8% of matches.
Non-elite tennis tournaments show similar inefficiency. Favourites priced under 1.50 lose more often than implied probability by approximately 2.3–2.9 percentage points across a sample of 68,000 matches.
Combat sports show the strongest emotional distortion. Fighters with large social media followings receive shorter prices than statistical models justify in 31–38% of observed bouts across a five-year sample.
Three patterns appear across long-term datasets.
- Famous teams receive systematically shorter odds in domestic leagues
- Popular athletes receive inflated prices outside elite tournaments
- Opening odds show greater distortion than closing odds
These effects repeat year after year.
Comparative snapshot from long-term datasets
The table below summarizes patterns found in aggregated bookmaker and exchange data covering full seasons across different sports.
| Sport category | Avg margin (%) | Odds correction speed | Favorite bias | Liquidity depth | Inefficiency persistence |
| Elite football | 3.8–4.6 | Fast | Moderate | Very high | Low |
| Lower-tier football | 6.2–7.9 | Moderate | High | Medium | Medium |
| Top-tier tennis | 4.9–5.6 | Fast | Moderate | High | Low |
| Non-elite tennis | 6.8–7.7 | Slow | High | Medium | High |
| Combat sports | 8.4–10.2 | Slow | Very high | Low | Very high |
| Regional niche sports | 7.5–9.3 | Slow | High | Low | High |
These values come from multi-season market observations, not theoretical models.
Bookmaker margins shape how fast prices correct
Margin size directly affects how long mispricing survives. Higher margins mean wider spreads and slower absorption of information.
Elite football markets often operate at 102–105% book. Lower-tier basketball, handball, futsal, and volleyball markets often sit between 108–113%. That difference delays correction measurably.
Historical odds logs show that lineup news in elite football moves prices within 30–90 seconds. In low-liquidity regional markets, the same type of information can take 8–15 minutes to fully reflect in pricing.
Three measurable effects appear consistently.
- Higher margins correlate with slower odds movement
- Low liquidity increases duration of price distortion
- Public betting volume exaggerates favourite prices early
These effects appear across sports and across seasons.
Closing line behaviour exposes long-term structure
The closing line reflects collective market consensus. It incorporates late information, sharp action, and public correction. The distance between opening and closing prices reveals market inefficiency.
Large datasets comparing opening and closing odds show consistent gaps by sport.
- Elite football: average movement 1.4–1.9%
- Lower-tier football: average movement 3.8–5.6%
- Top-tier tennis: average movement 2.0–2.7%
- Non-elite tennis: average movement 4.1–5.3%
- Combat sports: average movement 6.8–9.2%
These numbers appear across samples exceeding 500,000 events in total. Three long-term observations emerge.
- Opening prices contain more emotional bias
- Late movement reflects sharper information
- Low-liquidity sports maintain wider opening distortion
This pattern appears consistently across datasets.
Structural bias patterns that repeat every season
Long-term datasets also reveal specific behavioural distortions.
Newly promoted football teams receive overly pessimistic pricing during the first three rounds in 64–71% of seasons studied across multiple leagues. Prices adjust upward only after real performance data accumulates.
Tennis players returning after injury receive inflated market support. Across a five-year dataset, returning players priced below 2.00 lost 7–11% more often than implied probability during their first three matches back.
Combat sports show the strongest reputation distortion. Fighters with over one million followers on major platforms received shorter odds than comparable opponents in 72% of analyzed bouts. That bias shrinks only close to fight time.
What the data actually demonstrates
The numbers do not suggest that any sport is “easy.” They show something more subtle.
Some markets self-correct rapidly. Others tolerate distortion for longer periods. That tolerance creates longer-lived inefficiencies.
Sports with large global attention become efficient quickly. Sports with fragmented audiences remain inefficient longer. Sports driven by star personalities remain emotionally distorted longer. Markets with low liquidity adjust slower than markets with deep volume.
The evidence remains consistent across seasons, competitions, and datasets exceeding millions of price points.
Value betting does not depend on belief. It depends on structure. Historical data exposes that structure clearly.
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