With 105 countries listed as active members of the ICC (International Cricket Council), cricket is easily one of the most popular sports in the world.
Being such a popular sport, with many competitions and tournaments held each year, cricket is a magnet for gamblers who enjoy placing bets on the outcome of upcoming matches. However, while correct predictions can lead to sizable windfalls, getting it wrong could be more costly than most would care to admit.
Because of this, those looking for today’s match winning probability are constantly seeking ways to improve their bet accuracy and find new systems to help streamline bets and predict the outcomes of games long before they occur. Although many systems like this exist, few are accurate enough to lead to large winnings.
However, the evolution of a mathematical algorithm called win probability is changing this. This algorithm makes match predictions more precise because they can factor in live events during a match into existing bet recommendations. Read on to discover what win probability is and how it is transforming the way people place bets on the sport.
What Is Win Probability?
The origins of probability as a theory, developed by Pierre-Simon Laplace, date back as far as 1654. Since then, the science of probability has been applied to many areas to help assist in predictions and statistics and to make algorithms more efficient.
Win probability is not a new extension to the concept. Although it is unknown when it was first developed, win probability has been used in the gambling industry to determine the odds of one player besting another. It is only recently, however, that this has been applied to sports.
In essence, win probability is nothing more than a statistical tool. It uses historical team data to try and predict a team’s chance of winning using live data from the match they are engaged in. Cross-referencing this data, a win probability algorithm can make predictions that gamblers can use to adjust their bets—particularly if they use a bookmaker that supports live betting.
How Does Win Probability Work?
Although the precise workings of an algorithm like this may only be fully understood by those with an advanced understanding of mathematics and data analysis, let’s simplify how win probability works.
This statistical tool uses certain data sets to identify critical elements that shape the probability model. In cricket, some of these data sets are the players on each team, the current run rate of a team, how many overs are left in a match, the pitch and weather conditions, and whether the game is played at a home stadium or away.
Each data set is assigned a probability factor based on historical data. This factor is based on how often a win has previously occurred under set conditions or on an impact percentage allocated to the data set.
To fully understand how one of these data sets receives its probability factor or impact percentage to integrate into the final model, let’s look at the example of players.
Data Set Analysis
Calculating the probability factor for a single player first requires identifying the impact percentage that a player has on a game. To arrive at this information, a relatively simple formula can be used. However, this formula must be calculated across several matches to obtain an average—the more games used in this process, the more accurate the average will likely be.
Determining this impact percentage requires analysing game data, such as when a player (in this example, a batter) is brought onto the field. This data can be accessed from any cricket information website.
Using the score when this player arrives and the score after he has been dismissed or the game has ended, a variable can be calculated on what impact this particular player has had on the game (e.g. he achieved 52 runs from the total of 235 scored). His impact would be 8.5% of the total runs in this example.
As mentioned, applying this to multiple matches the player has participated in will grant an average impact percentage that can be used in a data set as a factor for that player.
Win Probability Model
Once the laborious task of assigning each data set a unique identifier in the form of a probability factor or impact percentage is done, these sets are ready to be used in the primary win probability model. To do this, all data sets relevant to a particular game are considered against each other.
Using the historical data and information from each data set, the algorithm can then identify which team will most likely win. As the match progresses and players are bowled or caught out, runs increase, or conditions change, alternative data sets can be added or removed accordingly.
Each time a data set changes, the win probability algorithm recalculates the new information and generates a new win probability percentage. Because it is based on factual past information, the probability percentage generated by these algorithms is surprisingly accurate. However, like anything else in life, 100% accuracy is impossible to achieve when working with predictions and probability.
However, for gamblers looking for improved betting accuracy, these algorithms and models are among the best sources of information.
The Downside of Win Probability
Although the cutting-edge mathematics behind win probability can help generate more accurate predictions and, in turn, more accurate bets, there is one catch. Compiling the information required to create accurate data set factors is exhaustive and time-consuming.
While AI and machine learning have sped up this process significantly by inherently being able to predict odds, a massive amount of human interaction and intervention is required. Compiling even a small collection of data sets could take days or weeks.
Because of this, win probability has not been widely rolled out in a manner easily accessible to everyday consumers. Although this will likely occur in the future, gamblers need to generate their own data sets for now and hope they can get these ready in time to get their bets in for upcoming matches.
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