As Roland Garros approaches, Sportradar and ATP data partners point to a shift towards predictive models, richer player markets and growing safeguarding challenges across tennis.
As the tennis calendar turns towards Roland Garros, a quieter transformation is taking place off court. Data, long a foundation of sports betting, is now being repositioned as a predictive tool capable of reshaping how tennis is consumed, traded and safeguarded.
At a Sportradar-hosted event on 27 April, senior executives from across the tennis data ecosystem outlined how the sport is moving beyond reactive, point-by-point information towards more sophisticated modelling. The shift, they suggested, is already influencing how betting markets are structured and how fans engage with the game.
Rainer Lichtmannegger, SVP Sports Content Products at Sportradar, described the transition in simple terms: “We’re shifting from reactive data points to predictive data points.”
This evolution is underpinned by a steady expansion in the depth and type of data collected across the ATP Tour. What began with basic inputs from umpire chairs more than a decade ago has developed into a system capable of capturing granular player actions and contextual match insights in real time.
“The more data you have, the more sophisticated your models can be,” Lichtmannegger noted, pointing to its relevance for live odds and in-play markets.
Building new betting markets
The commercial impact of this data expansion is most visible in how tennis betting markets are being redefined. Traditionally centred around match winners, operators are now offering a growing range of player performance-based options.
“There was a clear gap,” Lichtmannegger said, referring to bettors who wanted more than outright markets. “Now there is a player performance market so you can build your own story.”
This shift aligns with a broader trend in sports betting towards personalisation and micro-markets, where users can engage with specific moments within a match rather than its final outcome. In tennis, where matches are naturally segmented into points, games and sets, the structure lends itself to this level of detail.
Lichtmannegger described tennis as “the perfect live betting sport,” highlighting both its format and its global schedule. With matches taking place daily across time zones, the sport offers continuous inventory for operators and sustained engagement opportunities for bettors.
A unified data ecosystem
Behind this shift sits a more coordinated approach to how tennis data is collected and distributed. Tennis Data Innovations (TDI), the joint venture responsible for ATP data and streaming rights, has focused on standardising processes across tournaments.
David Lampitt, CEO of TDI, acknowledged tennis had historically operated with a fragmented data footprint. The aim, he said, has been to “apply a unified approach to how we collect data and create a consistent product which we can do things with.”
Advances in tracking technology have played a central role, with electronic line calling systems, now widely deployed, automatically determining where the ball lands and converting those decisions into usable data streams. This information feeds directly into broadcast, betting and digital products.
Lampitt drew a comparison between betting markets and financial systems, noting “the tennis betting market doesn’t operate that differently to a financial market.” In other words, the more consistent and higher-quality data enables more complex trading environments.
Data as content
The growth in data is not only influencing betting markets. It is also reshaping how tennis content is created and distributed, particularly as fan behaviour shifts towards short-form and real-time consumption.
According to Lampitt, there has been a fivefold increase in how data is used to support content creation, from identifying potential upsets mid-match to generating post-match insights for social media.
“The way in which fans consume sports content has shifted,” he said, pointing to the growing importance of social platforms and the demand for more detailed, player-focused narratives.
This has also driven experimentation with AI tools, including chatbot integrations designed to make match data more accessible. While the technology is not yet capable of interpreting player psychology in real time, Lampitt suggested there remains “another layer of data to be exploited.”
Safeguarding in a data-driven environment
However, increased visibility and engagement bring additional challenges. Separate sessions at the event focused on the scale of online abuse directed at tennis players and the role of technology in mitigating it.
Adam Pennock, VP Risk & Investigation at Sportradar, revealed 6.5% of social media comments linked to ATP conversations are abusive, equating to more than 116,000 accounts. The issue, he said, is not limited to isolated incidents.
“Persistent, methodical and calculated” behaviour from a relatively small group of individuals has been identified, with some operating dozens of accounts to target athletes across sports.
In response, Sportradar has developed a multi-layered system combining AI monitoring, automated moderation and escalation protocols. Comments are categorised in real time, with harmful content filtered before reaching players who opt into the service. The system also extends into real-world safeguarding. In one case cited, a death threat identified through social media monitoring led to the arrest of an individual attending a tournament in the same city as the targeted player.
Andrew Azzopardi, Director of Safeguarding at the ATP, stressed online abuse should not be dismissed as background noise. “It is constant. It is demeaning,” he said, framing the issue as a broader societal challenge rather than one confined to sport.
