Stats Perform

Andrew Ashenden, Stats Perform: Delivering a superior player prop experience

We spoke to Andrew Ashenden, Chief Betting Officer at Stats Perform, about why VQ – a new API service already live for NFL and NBA player performance lines – is boosting the betting experience for punters and solving a previous pricing problem for operators.  

SBC: In the simplest terms, what is VQ?

AA: VQ is Stats Perform’s advanced player performance predictions service, which uses proprietary sports data and A.I. to help operators post player prop lines earlier, for more players.  

SBC: Why have you launched it? 

AA: Influenced by fantasy sports and social media, fans and bettors follow individual players as much as teams. Because player prop lines can be difficult to price, sportsbooks haven’t yet been able to keep-up with the accompanying demand for wide coverage of player performance prop bets. 

VQ empowers them to do this by delivering automated high-grade predictions and probabilities in multiple stats categories.

SBC: What problems does it solve for operators?

AA: Player prop lines can be difficult to price. Compared to traditional bet options they require a lot of data, a lot of processing, and there is often a lack of market comparables. If there is a late injury or team change, it has historically meant pulling markets down to recalculate the impact. 

VQ’s comprehensive set of predictions and probabilities gives odds compilers a critical early reference to confidently sharpen their player prop models, set prop lines earlier and for more players, and react fast to changes. With more confidence in the lines comes the ability for operators to increase bet limits, to take positions, and better manage their profitability.

SBC: What impact will bettors notice? 

AA: Bettors at operators using VQ will notice an earlier and wider availability of player props across more players; not just a drip-fed handful of late options for star players. Even if there are late injuries or team changes, they’ll still be able to quickly find player performance lines for their favourite players.

SBC: How is it different to player prop services on the market?

AA: VQ has been set up in the following way:

  1. It uses deep, accurate, unique player data (including complex, non-linear player performance patterns), which we collect in-house
  2. This deep data is used to ‘train’ sophisticated AI models 
  3. The AI models quickly and efficiently analyse patterns in the data and derive highly specific predictions across multiple stats categories for a vast range of players
  4. Predictions for more players means prop markets for more players
  5. Collecting and processing our own data generates efficiencies which means we can release our predictions earlier, in bulk, and react faster to changes.

SBC: And why are you set up to stand out in this way?

AA: The vast number of players, permutations and games means most sportsbooks’ player prop offerings are limited. Yet, Stats Perform has always had the data, and now our investment in AI to derive predictions from it at scale provides our customers with increased confidence to deliver a superior player prop experience for their bettors.

SBC: What sports does it cover?

AA: VQ is live now for NFL and NBA player performance lines, with more sports in development.

SBC: And finally, what have your partners said about it?

AA: We’re excited with the feedback we’ve received from operators who share our vision for the role AI can play in driving betting experiences forward. AI allows us to start to leverage our deep proprietary data’s full predictive power for the first time. 

It is virtually impossible for humans to do this level of analysis efficiently. By using AI in VQ, we can derive highly specific predictions from the data for more players in a specific match situation, not just the star players, and not just using high-level trends.

For further information on the product, visit our dedicated product page here.

SBC News Andrew Ashenden, Stats Perform: Delivering a superior player prop experience

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