Wagering Odds Adjustment via Player Sensor Data
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Solution Overview
Problem
Current sport betting platforms calculate wagering odds based on team-level statistical analysis, which does not incorporate player-level data, leading to inaccuracies in play-by-play odds determination and potential improvements through player data collected from sensors.
Innovation Solution
A system that utilizes sensors to collect player-level data during live events, which is then analyzed to adjust wagering odds in real-time, incorporating data from historic sensor databases to correlate player actions and update odds accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If team-level statistical analysis is used to calculate wagering odds, then the calculation process remains simple and data requirements are minimal, but the accuracy of play-by-play odds determination deteriorates due to lack of player-level data
Solution Approach 1:
The system segments team-level data into player-level data by collecting individual sensor data for each player (position, speed, distance, direction) and analyzing it separately before aggregating to team level, enabling play-by-play accuracy while maintaining manageable data processing structure
Solution Approach 2:
The system introduces sensor data as an intermediary between player actions and wagering odds calculation, using wearable sensors and field sensors to capture detailed player-level information that mediates between raw player actions and the final odds determination
2Measurement precision
If player-level sensor data is collected and analyzed in real-time, then the accuracy and responsiveness of wagering odds improves, but the data processing complexity and computational requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-defining player roles, positions, and action types before the event occurs, and pre-establishing correlation models between sensor data and play outcomes, so that during live events the processing focuses only on matching pre-defined categories rather than analyzing everything from scratch
Solution Approach 2:
The system applies partial action by selectively processing only the sensor data relevant to current play situations (e.g., only tracking players involved in active plays rather than all players continuously), and uses excessive sampling at lower resolutions for non-critical data to reduce overall processing load
3Speed
If real-time sensor data is integrated into wagering odds, then the responsiveness to live event changes improves, but the system complexity and infrastructure requirements worsen
Solution Approach 1:
The system implements feedback loops where sensor data continuously feeds into the odds calculation system, which then updates wagering odds in real-time based on current player actions and positions, creating a closed-loop system that responds automatically to live event changes
Solution Approach 2:
The system uses universal sensor platforms and standardized data processing pipelines that can handle multiple types of sensors (wearable, field, video) and multiple sports events through a single unified infrastructure, reducing overall system complexity despite real-time processing requirements
Data Source
AI summary
A system involving analytics and collecting sensor data in real time. This system allows players to predict and wager on players actions during the course of a play that has yet to occur by collecting sensor data on the players to create a historical database. Utilizing an algorithm, the wagering odds may be improved using the various sensor data collected using artificial intelligence or machine learning. The algorithm may determine the probability of the outcome of the play through player's sensor data and these probabilities of the outcome provide additional data for a wagering platform to provide improved wagering odds to its users.