WVU researcher dissects statewide iGaming data, finds concentrated high-frequency play

West Virginia University doctoral researcher Matt Mullis has analysed statewide gambling transaction data made available after the Responsible Gaming Research and Industry Development Act passed in 2024, producing granular findings on iGaming and video lottery behaviour across West Virginia. His work shows average mobile slots bets of $1, a single operator recording 43 million mobile slots bets in one month placed by roughly 16,000 players, and clear demand spikes on Social Security payment days for Hot Spot terminals.

How WVU got the data and what it covers

The Responsible Gaming Research and Industry Development Act of 2024 authorised West Virginia University to access transactional data on all gaming activity statewide in partnership with the State Lottery Commission. The WVU Center for Gaming Research and Development now receives machine-level and account-level feeds that let researchers trace activity from broad revenue totals down to individual bets.

Professor Brad Humphreys, director of the Center for Gambling Research and associate dean for research at the WVU John Chambers College of Business and Economics, provided the initial dataset of daily machine-level revenues from West Virginia video lottery terminals between 2024 and 2025. That early access let graduate students identify patterns that typical lab experiments or surveys cannot reveal because real-world sample sizes are vastly larger.

Key empirical findings from Mullis' work

Mullis highlights several concrete metrics drawn from the dataset:

• The average mobile slots bet in West Virginia is $1.

• One operator recorded 43,000,000 mobile slots bets in a single month, generated by about 16,000 distinct players.

• On Social Security payment Wednesdays (every second, third and fourth Wednesday of the month) demand for Hot Spot slot machines increases.

• The average West Virginia Hot Spot terminal takes in about $1,000 a day before taxes.

• West Virginia taxes Hot Spot revenue at approximately 50%, while iGaming revenues face about 10% tax.

Mullis also reports that, in one month of the mobile slots sample, 25% of bettors placed more than 1,000 bets each.

Behavioural signals: loss-chasing, whales and temporal effects

Mullis uses the term “loss chasing” to describe the sequence where players increase wager size after losses in an effort to recoup funds quickly. His datasets reveal both macro and micro indicators relevant to that dynamic: repeated rapid refills of accounts, concentrated wagering within short time windows, and the presence of high-frequency “whales” who deposit hundreds of dollars nearly every day.

He can link large-scale phenomena to specific events. For example, slot-machine play dips on days when West Virginia University or the Pittsburgh Steelers are playing, indicating audience attention shifts away from slot play during big sporting events. Conversely, Social Security payment dates correlate with higher Hot Spot activity, which Mullis interprets as partial channeling of transfer payments into gambling on arrival days.

"People enjoy the act of betting," Mullis said. "But if someone wins and then we see that they bet more, are they betting more just because they have more money to bet with? Or are they betting more after they win because they feel like they're hot?"

That quote encapsulates the empirical problem: the data show behavioural changes after outcomes, but distinguishing rational liquidity effects from biased belief updating is difficult even with large datasets.

Practical applications: flagging and short breaks

Mullis outlines downstream uses for the research data that operators and regulators could implement. One example is automated flagging of potentially problematic iGaming behaviour: repeated account refills within an hour, wagering thresholds like $1,000 in 30 minutes, or extreme bet-counts in a short period. These triggers could feed into enforced short breaks in play or targeted responsible-gambling interventions.

The Center's remit is research rather than rulemaking, but the insights are structured to inform policy and operator practice. Mullis emphasises that the aim is to identify measurable behaviours that correlate with harm and to provide evidence for proportional mitigations rather than to prescribe specific product-level controls.

The commercial environment and operator tactics

Mullis describes the modern market as characterised by easy bank linking and promotional incentives. He names DraftKings and FanDuel as examples of operators that use "Free Bet" promotions and frictionless payment rails to encourage continued play. He also says he conducts hands-on fieldwork: occasionally placing dime-sized bets in casino apps such as Caesar Sports to better understand the user experience.

Marketing intensity is another variable in Mullis' analysis. He cites research showing an average of 3.5 references to gambling per minute in some National Hockey League broadcasts, and he links rising U.S. gambling activity to broader legalisation and mobile accessibility, including record wagering on the 2026 World Cup.

Methodology limits and ethical questions

Mullis notes methodological constraints despite the unusual data access. Most behavioural identification comes from observed sequences of bets and deposits rather than direct measures of intent. That limits the ability to infer why a player increases stake size after a win or loss. He underscores a normative tension: state lotteries and gaming generate substantial revenue for infrastructure and social programmes, yet participation sometimes concentrates among people least able to afford losses.

He says the Centre's data are designed to inform those moral and policy questions. The research does not resolve them but provides evidence policymakers can use when weighing taxation, venue regulation, or mandated product safety measures.

What operators and regulators should watch next

Operators should expect scrutiny of rapid funding and wagering behaviours and possible adoption of short-break mechanics triggered by quantitative flags. Regulators and the State Lottery Commission will likely review the Centre's future outputs as empirical input for any prospective safeguards.

For readers tracking product design and compliance, the findings underscore two linked pressures: increasing mobile play volumes and the visibility of concentrated high-frequency betting patterns that can be monitored and, if regulators choose, regulated.