But these tips are far from providing accurate predictions to the numerous bettors across Mauritius. Many people around the world have been studying the horse racing business and have subsequently developed several algorithms to forecast winning horses accurately. In this review, the approaches used by different researchers to make their predictions on different sports have been investigated and discussed.
Williams and Li studied horse racing in Jamaica in order to find probable winners . The main methodology used was Artificial Neural Network, an adaptive system that is trained from existing data and can predict results when offered with new input patterns. In Jamaica, thoroughbred horses race over distances of m to m. Data from races were collected from 1st January to 16th June Four different learning algorithms were applied to the data and the results obtained were compared.
Each algorithm had to find patterns in the data provided and forecast the winners in each race. They studied different variables connected to horse racing such as racing distance, past position, weight of horse and jockey, horse's finishing time and others as input to the Neural Network.
Baulch investigated the use of Artificial Neural Network in rugby league and basketball games . He used two types of neural networks to predict winners in both games. The data used for analysis were the team's previous performances, the number of wins and the average scores per game to predict the winner. He concluded that neural networks helped a great deal in sports predictions and better results can be obtained from more indepth research. Chen et al. Initially, they found it very difficult to apply neural network algorithms as there are about 50 different variables available to bettors on race cards .
The neural network cannot use all these variables to predict accurate results and thus they decided to filter them out and analyse only what they thought to be the most important variables. Data from races and greyhounds were gathered for training.
An additional races with greyhounds were used for testing purposes. The system forecasted winners for 34 races, but wrongly predicted 50 results and no winners were predicted for 16 races. They placed bets on dogs that were predicted winners by the system. These values clearly showed that the neural network predictions outperformed those of the human experts.
Davoodi and Khanteymoori made horse racing predictions for a race track in New York using neural networks . Data for races were collected for analysis from 1st to 29th January Artificial Neural Networks were used to forecast the finishing time of each horse in a race.
Supervised learning has been used where the neural network was provided with training data and the desired output. Five supervised algorithms were used in their study to compare their forecasting power and performances. Eight variables had been used as input: Horse weight, type of race, horse's trainer, jockey, number of horses in a race, race distance, the track condition and weather. These data were input and one neural network was used for each horse.
The finishing times along with the horses' ranks were then predicted. Pardee investigated the use of neural networks to predict outcomes of football matches in college . He developed a model using data from past football seasons to find out the winning team. He examined data from 11 games for each team which played in the and seasons.
He used those historical data to train and test the system. Upon comparing the results of his predictions with those of a television channel, he found that the latter's forecast were only accurate up to He also stated that if given more data about the team's previous performances, the degree of accuracy of his system could have been higher. McCullagh examined the use of neural networks to support the data mining process for the talent identification problem to select potential players for the annual Australian Football League .
He used a data set of players and 58 attributes which have been tested from to He carried out an analysis to compare the results from recruiting managers and the neural network. Data for 20 players and recruiting managers from 16 different clubs were selected for the study.
However, it was found that in most of the cases, the recruiting managers gave the most correct predictions This can be explained by the fact that the neural network depend data like body composition, flexibility, anaerobic and aerobic power, visual tests, skill assessments and subjective assessments on strengths, weaknesses and personal attributes of the players while the recruiting managers have widespread networks of people watching games of prospective players over many years, interviews and videos as well.
Hence, he could only conclude that the neural network can be used by the recruiting managers to support and enhance their decisions before choosing players but cannot replace the expertise of the recruiting managers. Several interviews have been carried out with people from the horse racing business to find out the factors that affect a race. The results of races in have been thoroughly examined to find out to which extent the factors affect a race. Furthermore, all the data have been scaled to a range between 0 and 1 and weights have been assigned to each factor.
A software was developed to enable users to input details about new races and to make predictions about potential winners. The factors that affect a race are discussed below as well as the algorithm that has been used to allocate weights to them. Factor 1: JockeyThe jockey is an important determinant that greatly affects the winning chance of a horse. The performance of each jockey in a race varies. The weights attributed to each jockey will be the total wins of the jockey divided by its total mount.
Factor 2: New horseAn interesting trend has been found when analysing past results of races over 3 consecutive years. Factor 3: OddsThe odds at which a horse is offered can be very indicative of its actual chance of winning. Normally, bookmakers will offer the lowest return on a horse which according to them has the highest chance of winning the race. Therefore, as a general principle, the lower the odds of a horse, the higher is its chance of winning.
Furthermore, it has been concluded from the research made on the results of 3 consecutive years that on average Thus, the algorithm to be applied is: find the lowest odd and divide the odds of each horse by the lowest odd and multiply by 0. Factor 4: Previous performancesThis factor takes into consideration the previous performances i. We consider only the last three performances of each horse. The algorithm is as follows:Rank1 is the rank of the horse in the last race that it ran, etc.
The algorithms proceeds as follows: i compute a total for each horse in a race ii find the lowest total iii divide the lowest total by the total of each horse. Factor 5: DrawThe draw is the starting stall on which a horse has been placed and it is a determining factor. The idea here is to study the frequency of winning of horses starting from particular draw in relationship to the number of horses participating in the race.
For example, calculating the number of times that a horse starting from draw 1, wins the race when there are 8 horses. The percentage must be found out and assigned to the horses when they take part in a race, depending on their draw and number of horses taking part in the race. However, it is possible that a horse is classified in more than one category.
The classification of each horse is done based on its previous performances on different distances. A horse will be classified in a category in which it performs best. Thus, the algorithm is shown below: Factor 7: WeightThe lowest weight that can be carried by a horse during a race is 49 kg while the highest weight is 62 kg.
It is reasonable to assume that as the weight gets down, a horse will generally perform better. Factor 8: RatingThe rating is an already computed value which shows the strength of a particular horse. Thus, by principle, the higher the rating of a horse is, the stronger it is. Thus, the algorithm is to divide the rating of each horse by the highest rating among the horses participating in the race. Factor 9: StableSome stables have better reputation than others because their horses win more races than the others.
This is usually due to better treatment and better training. Thus, the algorithm is to take the percentage wins of each stable and assign it to the horse. G Legras. G Legras 6. Marseille-borely Hp De Marseille Handicap. V Seguy. M Germain 9. M Barzalona. M Waldhauser. Bet Credits available for use upon settlement of bets to value of qualifying deposit.
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Forgot your password? Sign up using our simple one-page form and you'll be able to access free video form, tips and exclusive content straight away. Track this horse Track this horse. All Distances 1m 1m-1m2f 1m2f-1m4f. All Configurations Right-Handed. All track types Level. All Classes. Summary - Data does not include this horse 24 runs, 4 wins 3 horses , 7 placed, 13 unplaced.
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Next time out 10 runs, 0 wins, 2 placed, 8 unplaced. Date days since Race Details Wgt. Summary - Data does not include this horse 38 runs, 4 wins 4 horses , 6 placed, 28 unplaced. Next time out 13 runs, 3 wins, 4 placed, 6 unplaced. Index value from 13 horses.
Summary - Data does not include this horse 38 runs, 2 wins 2 horses , 5 placed, 31 unplaced. Next time out 9 runs, 0 wins, 3 placed, 6 unplaced.
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