Systems and methods for real-time rebalancing of bet portfolios in the pari-mutuel bet environment

1 . A method, comprising:

iteratively identifying, by a computer system including one or more processors, in real-time, live odds of the horse racing event, wherein each iteration includes:

monitoring a plurality of electronic wagers submitted by a plurality of electronic devices corresponding to the horse racing event, each electronic wager identifying at least one horse and a monetary amount associated with a bet on the at least one horse;

calculating, based on monitored data, live data comprising win odds, probables data comprising expected payouts for single event wagers, will pays data comprising expected payouts for multi-race wagers;

calculating an implied probability of winning in one or more first pools of the horse racing event based on the calculated live data, probables data, and will pays data; and

executing a feed-forward neural network using monitored data to predict one or more missing data points to calculate implied win probabilities in one or more second pools of the horse racing event for which odds or probables data is not available;

receiving, by the computer system, from a client device, a wager request for the horse racing event, the wager request comprising a request for a betting strategy recommendation based on undervalued horses;

generating, by the computer system, a real-time bet package including a wager recommendation for the client device based on the live odds of the horse racing event and estimated odds for horses participating in a race identified in the wager request; and

transmitting, by the computer system to the client device, the real-time bet package for display.

2 . The method of claim 1 , comprising:

calculating, by the computer system, for each horse participating in the race identified in the wager request, corresponding estimated odds for the horse using a machine learning model trained using historic racing data.

3 . The method of claim 2 , wherein the machine learning model includes a decision tree model.

4 . The method of claim 1 , comprising:

identifying, by the computer system, one or more horses participating in the horse racing event having live odds greater than estimated odds for the one or more horses as undervalued horses; and

generating, by the computer system, the real-time bet package for the client device based on the one or more horses identified as undervalued horses.

5 . The method of claim 4 , wherein the one or more horses are one or more first horses and the method comprising:

detecting, by the computer system, a change in the live odds; and

in response to detecting the change in the live odds, identifying, by the computer system, one or more horses second horses as undervalued horses;

generating, by the computer system, a second real-time bet package for the client device based on the one or more second horses identified as undervalued horses; and

transmitting, by the computer system to the client device, the second real-time bet package for display.

6 . The method of claim 4 , further comprising:

estimating, by the computer system, an expected payout value for each horse of the one or more horses identified as undervalued horses.

7 . The method of claim 6 , further comprising:

sorting, by the computer system, the one or more horses identified as undervalued horses based on corresponding expected payout values.

8 . The method of claim 6 , wherein estimating an expected payout value for an undervalued horse includes:

calculating, by the computer system using a machine learning model and the live odds, a plurality of outcome predictions of a plurality of betting strategies; and

determining, by the computer system, the expected payout for the undervalued horse based on the plurality of outcome predictions of the plurality of betting strategies.

9 . The method of claim 1 , wherein calculating the implied win probabilities in one or more second pools includes using a machine learning model to determine payout odds for the one or more second pools.

10 . The method of claim 9 , wherein the machine learning model includes a feedforward neural network.

11 . A system, comprising:

one or more processors; and

a memory storing executable instructions, the executable instructions when executed by the one or more processors cause the one or more processors to:

iteratively identify live odds for a horse racing event, wherein in each iteration the one or more processors:

monitor a plurality of electronic wagers submitted by a plurality of electronic devices corresponding to the horse racing event, each electronic wager identifying at least one horse and a monetary amount associated with a bet on the at least one horse;

calculate, based on monitored data, live data comprising win odds, probables data comprising expected payouts for single event wagers, and will pays data comprising expected payouts for multi-race wagers;

execute a feed-forward neural network using monitored data to predict one or more missing data points to calculate implied win probabilities in one or more second pools of the horse racing event for which odds or probables data is not available; and

calculate implied win probabilities in one or more second pools of the horse racing event for which odds or probables data is not available;

receive, from a client device, a wager request for the horse racing event, the wager request comprising a request for a betting strategy recommendation based on undervalued horses;

generate a real-time bet package including a wager recommendation for the client device based on the live odds of the horse racing event and estimated odds for horses participating in a race identified in the wager request; and

transmit, to the client device, the real-time bet package for display.

12 . The system of claim 11 , wherein the one or more processors are configured to:

calculate for each horse participating in the race identified in the wager request, corresponding estimated odds for the horse using a machine learning model trained using historic racing data.

13 . The system of claim 12 , wherein the machine learning model includes a decision tree model.

14 . The system of claim 1 , wherein the one or more processors are configured to:

identify one or more horses participating in the horse racing event having live odds greater than estimated odds for the one or more horses as undervalued horses; and

generate the real-time bet package for the client device based on the one or more horses identified as undervalued horses.

15 . The system of claim 14 , wherein the one or more horses are one or more first horses and wherein the one or more processors are configured to:

detect a change in the live odds; and

in response to detecting the change in the live odds,

identify one or more horses second horses as undervalued horses;

generate a second real-time bet package for the client device based on the one or more second horses identified as undervalued horses; and

transmit, to the client device, the second real-time bet package for display.

16 . The system of claim 14 , wherein the one or more processors are configured to:

estimate an expected payout value for each horse of the one or more horses identified as undervalued horses.

17 . The system of claim 16 , wherein the one or more processors are configured to:

sort the one or more horses identified as undervalued horses based on corresponding expected payout values.

18 . The system of claim 16 , wherein in estimating an expected payout value for an undervalued horse the one or more processors are configured to:

calculate, using a machine learning model and the live odds, a plurality of outcome predictions of a plurality of betting strategies; and

determine the expected payout for the undervalued horse based on the plurality of outcome predictions of the plurality of betting strategies.

19 . The system of claim 11 , wherein in calculating the implied win probabilities in one or more second pools the one or more processors are configured to use a machine learning model to determine payout odds for the one or more second pools.

20 . A non-transitory computer-readable medium storing computer instructions, the computer instructions when executed by one or more processors cause the one or more processors to:

iteratively identify live odds for a horse racing event, wherein in each iteration the one or more processors:

monitor a plurality of electronic wagers submitted by a plurality of electronic devices corresponding to the horse racing event, each electronic wager identifying at least one horse and a monetary amount associated with a bet on the at least one horse;

calculate, based on monitored data, live data comprising win odds, probables data comprising expected payouts for single event wagers, and will pays data comprising expected payouts for multi-race wagers;

calculate an implied probability of winning in one or more first pools of the horse racing event based on the calculated live data, probables data, and will pays data; and

execute a feed-forward neural network using monitored data to predict one or more missing data points to calculate implied win probabilities in one or more second pools of the horse racing event for which odds or probables data is not available;

receive, from a client device, a wager request for the horse racing event, the wager request comprising a request for a betting strategy recommendation based on undervalued horses;

generate a real-time bet package including a wager recommendation for the client device based on the live odds of the horse racing event and estimated odds for horses participating in a race identified in the wager request; and

transmit, to the client device, the real-time bet package for display.