A final score tells you who won but seldom how well. Today’s sports betting is more and more dependent on statistical models, player data, and live information to calculate probabilities and determine odds.
How Data Influences Betting Markets?
Bookmakers do not only take a teams most recent form into account when calculating odds. Transitions in the playing staff, past encounters with strong teams, injuries and even the past encounters with weaker teams can also have an impact on the odds of a bet.
At VivatBet sports betting (Estonian: spordi panustamine) includes ice hockey, tennis, basketball and football. Information about live odds, past results and both future and current events are available in this section.
Past encounters with strong teams can have an impact on a team’s most recent results. For example four wins against weak teams might not tell as much about the team in question as two draws against top teams in the league.
In statistical analyses the importance of such encounters can be considered by placing more importance on results won against higher quality opponents.
The market can have an impact on the odds independent of the actual game. Heavy betting on a specific outcome can also cause the bookmaker to hedge his risk. Volatility in the odds does not always reflect a large shift in the actual probability.
Bonus Conditions as a Probability Problem
Having a bigger betting balance does not guarantee better chances of winning money. The welcome bonus VivatBet, for example, offers a first deposit match of up to 100€, but the terms are that you must place accumulator bets of at least three selections, with each one having odds of 1.40 or higher. There is also a 30-day time limit restricting the window to complete the wagering requirements.
An accumulator only pays out if every pick is successful. Even when each of three independent bets has a 60% chance of winning, the chance that all three win is just 21.6%; that’s why a bonus balance isn’t the same as cash you can withdraw straight away.
Selecting Statistics That Explain Performance
In football, the number of shots a team takes tells only part of the story. Where those shots come from, and how often the team wins back possession can be just as important.
Tennis presents a different set of factors, with serve statistics and a player’s record on grass, clay, or hard courts often playing a larger role in match predictions.
Reading Football Data Beyond the Score
xG is an expected goal statistic that measures the probability of a shot creating a goal. It separates a team or player that creates a high volume of chances from one that scores exceptionally well from those chances.
Spain won EURO 2024 with 15 goals scored and Germany had a 91.2% passing accuracy. There were 51 games played during the tournament with a total of 117 goals scored or about 2.3 goals per game.
A few other metrics are also useful to compare this statistic to:
- Strength of opposition: The strength of the team played against
- Injuries / suspensions / rotation: Number of injuries, suspensions and rotation used
- Shot origin: Where shots were taken from
- Rest days: Number of days in between competitive matches
- Defensive transitions: Possession gained as a result of a defensive action
What Betting Odds Actually Measure?
Decimal odds express the total return on each unit staked, including the initial stake. You can find the implied probability of an outcome by dividing one by the price, but you also need to factor in the bookmaker margin when you do this.
The following hypothetical examples use a €10 stake.
| Decimal odds | Implied probability | Total payout |
| 1.50 | 66.67% | €15 |
| 2.00 | 50.00% | €20 |
| 2.50 | 40.00% | €25 |
| 4.00 | 25.00% | €40 |
Unsuccessful selections return nothing. Independent analysts compare estimated probabilities against available prices to assess potential value, while bookmakers must also consider margins and liabilities.
Machine Learning and Live Predictions
Machine-learning models can find relationships between variables that classical models do not. By learning from past data, they can also predict results for new matches, unseen in the past, but the ability to do so for our predictions again depends on the quality of that data.
Live forecasting requires updated inputs as matches develop. A typical analytical workflow includes:
- Collecting verified performance records.
- Adjusting for current team conditions.
- Estimating outcome probabilities.
- Comparing predictions with market prices.
- Testing forecasts against actual results.
Backtesting reveals whether a model performs beyond its training sample. Overfitting, missing injury information,n and changing tactics can undermine predictions despite strong historical results.
Regulation and Data Transparency in Estonia
The Tax and Customs Board in Estonia maintains a list of licensed gambling operators in the country. The winnings won via these licensed operators are exempt from personal income tax. Winnings from unlicensed gambling operators are not so exempt.
The operators must also use electronic systems for recording and control that are connected to the reporting system used by the Estonian authorities.
These systems must record gambling, bets and prize information and allow the authorities access to transaction data.