Last time the question was whether young men really are most at risk. This time it is what pulls them in, and why now. The usual answer is the phone, the apps, and an industry that spends a fortune telling young men to bet during the sport they were already watching.
That answer is probably right. But when I went looking for the proof, the strongest evidence turned out to be about things nobody files under advertising at all.
Here is a number I trust more than most of the ones that circulate. Researchers in Spain surveyed the students of fifteen high schools in Alicante, then went back a year later. The share who had ever gambled went from 28.5% to 42%. Among the 1,074 students they reached both times, the most common age to place a first bet was thirteen or fourteen.
That number comes with its population attached: who was asked, where, and when. Plenty of the figures about young men and betting lose that on the way to you.
A number missing four words
Here is one from this year's research on young men and betting ads: 77% of young men see betting ads every week.
It is real. It comes from a Canadian survey published in 2026, and I opened it. But the sentence is missing four words, and they change what it means.
The survey asked over 1,800 residents of Ontario and Alberta who had bet on sports in the past three months. Everyone in it was already a bettor. So the finding is that 77% of young men who bet on sports see betting ads weekly, which is a different and much less surprising claim. The press release dropped the qualifier. The search summaries copied the press release.
Within that sample the picture is consistent. Over half see live odds advertised during games. Three in five say the ads influence their betting. Three in five also say the responsible-gambling ads have not helped them cut down. Men under 30 are one in five of all the sports bettors surveyed, and report more gambling problems, more harm and more anxiety than the sample as a whole.
Those are worth knowing. None of them tells you whether the ads brought anyone in.
What two decades of research can and cannot say
The best source here is a review of the previous twenty years of studies on gambling advertising. It is not funded by the industry, and it is unusually blunt about its own field.
The review found 23 studies on gambling behaviour. In the ones that reported usable numbers, more exposure to gambling advertising goes with more gambling, at an average correlation of .24. That is a real and moderate relationship. It is also the best supported of the three things the review measured, because it rests on the most studies.
The other two are weaker than they are usually reported. The link with intentions comes out at .05, which the authors call small though statistically significant, and it rests on five studies. The link with attitudes looks the largest at .40, but it rests on two studies, and its confidence interval runs from −0.17 to 0.77. An interval that crosses zero means the data cannot rule out no relationship at all. That one should not be quoted as a finding.
Then the sentence that governs everything above. The review describes the field as having an absence of longitudinal and experimental studies.
Which means: as of 2020, almost every study was a photograph rather than a film. They show that the people who see more gambling ads also gamble more. None of them followed the same people long enough to see which of the two started first.
What changed after that
Three years later the same question got a bigger treatment: a review of eight systematic reviews, covering 74 studies between them. Its conclusion is that the evidence consistently supports the existence of a causal relationship between advertising exposure and gambling, that there is a dose-response pattern where more exposure means more participation and more risk of harm, and that there is more evidence of impact on children and young people, and on people already at risk.
That is a stronger verdict than the 2020 review reached, and it is worth knowing what kind of verdict it is. An umbrella review runs no new experiments. It reads the studies that already exist and judges what they add up to. The authors are open about this. They describe the evidence underneath as mostly snapshot surveys and qualitative studies of what people say they saw, and they reach their verdict, in their words, in the absence of definitive controlled studies: the association keeps showing up, and it grows with more exposure. That is a legitimate way to arrive at cause, and public health often has to. It is still a judgement about accumulated evidence rather than a controlled result.
And there is now some experimental-style work. A 2025 doctoral thesis used the 2022 World Cup as a quasi-experiment, following a panel of 365 men across 48 matches with matched comparison groups, and found that television advertising during the broadcasts influenced how often they bet and whether they bet at all, most clearly among higher-risk gamblers.
I opened the thesis rather than the abstract, and the detail matters. Everyone in that panel was a UK man aged 18 to 45 who already watched and bet on football regularly, recruited through an online research platform and weighted toward frequent gamblers. So it cannot show advertising pulling anyone into betting. It shows advertising moving people who were already betting. That is a real finding and it is a narrower one than the summary of it suggests.
That is the honest state of it. The evidence that advertising affects gambling has got stronger. Almost none of it was measured on young men as a group.
Which came first
This is the whole problem, and it is not a technicality.
If you survey a thousand people today and find the ones who see more betting ads also bet more, at least two stories fit perfectly. The ads could be pulling people into betting. Or people who already bet could be noticing betting ads, following the accounts, watching the sports where the ads run, and getting served more of them by platforms that know exactly what they engage with.
A snapshot cannot separate those. You would need to measure the same people over time, before and after, or change the ad exposure for one group and not another. That was the 2020 review's complaint about its own field.
Even the researchers slide
Here is the part I did not expect.
That Canadian fact sheet is real research, publicly funded, by a university team. Its own closing section says the advertisements appear to have a real impact on increasing sports betting, and describes mental health problems that both contribute to and result from gambling, creating a harmful cycle.
Both sentences describe causation. The study is a one-time survey of people who already bet. It cannot show a direction, let alone a cycle.
I do not think anyone is being dishonest. I think a fact sheet has to end with something, and "we found an association and cannot say which way it runs" is a bad closing line. But this is the same drift that shows up in AI summaries, appearing one step earlier in the chain. By the time a search engine repeats it, the overclaim is already in the source.
The pull nobody advertises
While I was checking the advertising numbers I kept walking past a bigger one.
A separate American survey asked over a thousand boys aged 11 to 17 how many of their friends gamble. Among boys whose friends mostly or all gamble, 84% gambled themselves. Among boys with a few gambling friends, 71%. Among boys with no gambling friends, 17%.
Seventeen to eighty-four. That is the steepest gradient in my library on what pulls boys toward gambling, and it has nothing to do with an advertiser. It is the other boys.
Two limits, and both matter.
The first is size. That 84% comes from 58 boys out of 1,017. A group that small moves a long way on a few answers, so the real figure could sit some distance either side of it.
The second is direction, and it is the same trap as the ads. Every boy was asked both questions in the same survey. So we cannot tell whether gambling friends pulled a boy in, or whether a boy who was already betting went and found friends who bet too. A bigger number does not escape that problem.
But it reframes the question. Advertising is the explanation the press releases reach for, and it is the one with a budget behind it. The friends are the bigger number, and nobody is paying to put them on television.
Why now, then
The timing question has better evidence than the advertising one, though still not the kind that settles anything.
In Australia, the share of men betting on sports went from 5.9% to 9.3% between 2015 and 2022, rising fastest among men under 45. That is a relative increase of more than half, and an absolute one of about three and a half percentage points.
In Switzerland, as the last entry covered, legalising online casinos raised participation and moved it online while the problem-gambling rate stayed statistically flat.
So access expanded, betting rose, and advertising expanded alongside both. Legal access, the phone in the pocket and the advert during the match all arrived at roughly the same time, which is exactly the condition under which you cannot tell which one did the work.
Except in one place, where they arrived separately enough to be told apart.
The phone
Of everything here, I expected the phone to be the hole. Every explanation of young men and betting leans on it: the app in the pocket, the bet that takes seconds instead of a trip to a shop. Nothing I had opened measured it, and I had written a paragraph saying exactly that.
That paragraph was wrong. The phone turns out to be the best-measured thing in this entry.
The reason is an accident of American law. After 2018, thirty-eight states legalised sports betting, but they did not all legalise the same thing. Thirty states and Washington DC allowed betting on a phone. Eight allowed it only inside a building. And many states switched on the building well before they switched on the phone.
That gap is a natural experiment. Hold legal betting constant, vary the phone, and watch what happens.
At least two groups have done it, using different data and measuring different things.
The first followed the credit records of about seven million Americans. In states that legalised betting at all, the average credit score fell by roughly 0.3%. In states that allowed it on a phone, it fell by about 1%, around three times as much, with bankruptcies, debts sent to collections and missed car payments rising behind it. Lenders responded by restricting credit.
The second used eight years of national health survey data and looked at drinking among men aged 35 and under. Legalising betting in general did nothing measurable. Legalising betting on a phone raised heavy drinking days by about 10% among the men who already drank heavily.
Two different datasets, two outcomes with nothing to do with each other, and the same shape. Legal betting barely registered. Legal betting in your pocket did the damage.
Two cautions. The credit study is a working paper and has not been through peer review, and it does not separate young men from anyone else. The drinking study is about alcohol rather than gambling, so it shows what followed the phone, not how much more anyone bet.
And one result in it does not fit the story this series has been telling. The effect shows up in men aged 25 to 34. Among men aged 18 to 24, there is no measurable effect.
The studies I said did not exist
I had an ending written for this entry. It said that when advertising restrictions get evaluated, somebody counts the advertisements and nobody checks whether anyone actually bet less. Then I went looking to be sure, and found two studies that had already done the work.
Spain restricted gambling advertising in November 2020. No celebrities, no shirt or stadium sponsorship, nothing on television or radio outside the hours of one and five in the morning, and no promotions aimed at signing up new customers. Researchers took eleven years of quarterly figures from the national gambling regulator and modelled what the market would have done if the law had never arrived.
Two things fell. New betting accounts, by about 263,000. Total money wagered, by about 216 million euros.
Two things did not move. The number of active accounts, and the amount people put into them.
That combination is unusually informative. The restriction reduced the number of people starting. It did close to nothing to the people already betting. For once a piece of evidence points straight at the question in the title: for people not yet in, the advertising does appear to be doing some of the pulling.
Two cautions. The law changed several things at once, daily deposit caps among them, so the advertising cannot be cleanly separated out. And a time-series model is an estimate of what would otherwise have happened, not a controlled trial.
The second study is the one from the opening. Those 1,074 Spanish teenagers were followed in exactly this order: advertising exposure measured first, gambling measured a year later, which is the time order most of this entry is missing.
The teenagers gambling frequently a year later had reported more advertising exposure at the start, and the difference was clear. Then the researchers accounted for everything else, and advertising dropped out of the model completely. What stayed in: whether they already gambled, their sex, how risky they believed gambling to be, and in one version, how many of their friends gambled.
I will not oversell that in the other direction either. Dropping out of a model is not proof that something does nothing, the advertising measure was teenagers estimating their own exposure, and the authors were careful to say that friends, thrill-seeking and easy access were interchangeable in that last slot. But it is the right design pointed at the right question, and it is the closest thing here to the study the question needs.
What counts as an ad
Put the last few sections beside each other and something awkward appears.
The steepest gradient in this entry is about friends. The best-measured effect is about a phone. Neither one is an advertisement. And the research I spent the first half of this entry weighing, the twenty years and the twenty-three studies, is mostly about advertising people could see and remember: commercials, sponsorship, social media posts, emails. Most of it worked by asking people what they recalled seeing.
Here is what the Spanish law did to what operators spent. Advertising fell. Bonuses fell. Sponsorship fell, after briefly rising in the months before its ban took effect. Spending on affiliates did not change.
Affiliates are people the operators pay to promote them and send traffic from their own channels to the betting site. In practice that means streamers, tipsters and social media accounts. It is not a side channel. Over the decade the Spanish study covers, operators' spending on affiliates went from 12 million euros a year to 42 million, about a third of what they spent on advertising by the end. The researchers' explanation for why the law missed it: there are a great many of these accounts, they vary widely, and their content is hard to control. They name Twitch, the streaming site, as the kind of loophole operators use.
Whether affiliates bring anyone in is still open. The work I could find on streamers and tipsters is snapshots: who watches, who follows, what they say they intend to do. I could not find a study that followed anyone from watching to betting.
The offer that arrives on your phone is a different story, and it has had the most direct test of anything in this entry. In a 2026 Australian experiment, 227 regular bettors were randomly split: one group opted out of every email, text and app notification from their betting companies, and the other carried on as normal. Over two weeks, the group that opted out placed 23% fewer bets, spent 39% less and reported about two-thirds fewer short-term harms.
It has limits, and they matter here. The bettors averaged 45 years old, and the study did not look at age or sex. Everyone in it was already betting, so like the World Cup thesis it shows marketing moving people who already bet, not pulling anyone new in. Many of the people assigned to opt out never sent proof that they had and were dropped, which weakens the random split the design depends on. The authors call their own analyses exploratory.
Put that beside Spain. A law aimed mostly at broadcast advertising left the people already betting alone. Switching off the messages aimed at them personally did not.
Then there are the explanations you hear most often, which sit in my library as four unchecked leads. Betting sold as analysis rather than luck, casting a man as someone who knows the game. In-play odds during the broadcast, framed as inside knowledge. Streak counters, bet builders and near-miss cues. Group chats where the wins get posted and the losses do not. I have opened none of the sources behind those four, so they stay leads, not findings.
Which makes me think "does advertising work" is the wrong-sized question. The broadcast advert looks like it brings people in. The personal offer looks like it keeps them betting. And the channel that grew fastest is the one the law could not reach and no study I found has followed through.
What I retired
Two claims came out of the library this week, both from my own earlier work.
The first said ad exposure is linked to gambling attitudes and intentions especially among young people. The review's strongest link is with behaviour, the attitudes estimate cannot be distinguished from zero, and the young-people emphasis is not in the source. The second said a UK campaign aimed at young male bettors produced more pausing before a bet. The source reports a level, not a change, and there was no baseline to compare against.
Both are gone. Neither had reached anything published.
Where this leaves the question
Four things now look true at once, and they do not sit comfortably together.
Broadcast advertising does appear to pull in people who have not started. Spain's restriction cut new accounts and left existing gamblers alone, and that is the clearest signal in anything I opened.
The marketing aimed at people already betting is a different thing, and it works on them. Switching off the personal offers cut how much regular bettors bet within two weeks.
The phone is worth roughly three times what legalisation on its own is worth, and nobody files the phone under advertising.
And the steepest gradient in anything I have read is other boys.
What none of it does is answer the question for young men as a group. The Spanish market data cannot be split by age and sex. The teenagers are teenagers, and that sample was 55% girls. The credit study separates nobody. The marketing experiment's bettors averaged 45. And the drinking study, which did split young men by age, found its effect among the 25 to 34s and none in the 18 to 24s.
One pattern did survive, narrower than I was going to claim it. In 2019 the UK's gambling industry brought in its own ban on betting adverts during live sport, and an independent evaluation found the ban did cut advertising around live football, while advertising around live horse racing went up instead. Horse racing was not covered by the rule. The advertisements did not go away. They moved to the sport the rule did not reach. What that evaluation counted was advertisements.
Italy went further, with a near-total ban on gambling advertising. I searched again, in English and in Italian, for an evaluation of what that did to betting, and could not find one.
Spain is the exception, and the reason is worth noticing. Its gambling regulator publishes the market's figures every quarter, and the researchers used those. They did not have to ask the industry for anything, because the government already had the numbers and put them out.
That is the most useful thing I found, and it is not about young men or about ads. Whether a restriction worked only gets answered where somebody is required to publish the numbers. Everywhere else, the explanation with a marketing budget behind it gets to stand in for the answer.
Sources and how I checked
Every number in this entry was opened at its original source. The table lines up what the AI search summary said against what the sources actually say. Where a figure was not opened, it is listed as unverified rather than used.
| What the search said | What the source says | How I checked |
|---|---|---|
| “77% of young men are exposed to betting advertisements on a weekly basis” | Right number, wrong population. It is 77% of young men WHO BET ON SPORTS. Everyone in the survey had bet in the past three months. The university's press release dropped the qualifier and the summary copied it. | Opened at source (full text (fact sheet)), 2026-10-25 |
| “60% report that advertisements influence their betting” | Accurate as a self-report, from the same group of existing bettors. The summary presents it as evidence of impact, which is what the respondents think is happening rather than what was measured. | Opened at source (full text (fact sheet)), 2026-10-18 |
| “Exposure to betting advertising encourages earlier initiation of sports betting” | Not opened, and the causal wording is the kind the field's own best review says has not been earned. That review describes an absence of longitudinal and experimental studies. | Not verified |
| “The rate of sports betting has surged more than 57%” | A relative figure quoted without the absolute one. Among Australian men the share betting on sports went from 5.9% to 9.3% between 2015 and 2022, which is about three and a half percentage points. The headline also drops the population. | Opened at source (full text (author's article)), 2026-10-18 |
| “38 states allow sports betting and 30 permit mobile betting; operators spend more than a billion dollars a year on advertising” | Half checked now. The 38 states and 30-with-mobile figures are confirmed in the credit-panel study (30 states plus DC allow online, 8 are retail-only). The billion-dollar advertising spend is still not opened. And the split the summary treats as trivia turns out to be the most useful thing in it: it is what lets anyone measure the phone separately from the betting. | Opened at source (full text (PDF)), 2026-10-20 |
| “Advertising exposure is linked to gambling attitudes and intentions, especially among young people” | This was my own earlier claim and it is now retired. The review's strongest link is with behaviour (mean r = .24, from the 23 behaviour studies that reported usable numbers). The attitudes estimate rests on two studies with an interval from -0.17 to 0.77, which crosses zero. The young-people emphasis is not in the source. | Opened at source (full text (author manuscript)), 2026-10-25 |
| “The causal link between ads and betting is still debated” | The most accurate line in the summary, and now slightly behind the field. A 2023 umbrella review of 8 reviews and 74 studies concludes the evidence consistently supports a causal relationship, with a dose-response effect and more evidence of impact on children, young people and those already at risk. That is a judgement about accumulated observational evidence, not a new experiment. | Opened at source (full text), 2026-10-25 |
| “35% of young men gamble daily or weekly, and 58% of UK boys aged 11-16 see gambling ads at least once a week on social media” | Not opened. A second search engine produced these while this entry was being edited, and neither figure matches anything in the sources I had already opened. They may well be real. They are not checked, so they are not used. | Searched; no research source found |
Sources
- Kim, A. (H.) et al. (TMU, with University of Bristol, Brock University, UMass Amherst, University of Calgary; Greo Evidence Insights). Sports Betting Harm in Young Men: The Impact of Advertising (fact sheet). April 2026
Measured on: Men under 30 within a survey of 1,800+ Ontario and Alberta residents who bet on sports in the past 3 months; subgroup n not reported (they are "1 in 5" of the sample) · under 30 · Canada (Ontario, Alberta) · What I read: Opened at source (full text (fact sheet)), 2026-10-25 · Funding: Canadian Institutes of Health Research and Alberta Gambling Research Institute - Bouguettaya, A., Lynott, D., Carter, A., Zerhouni, O., Meyer, S., Ladegaard, I., Gardner, J. & O'Brien, K.S. (2020). The relationship between gambling advertising and gambling attitudes, intentions and behaviours: a critical and meta-analytic review. Current Opinion in Behavioral Sciences 31:89-101. doi 10.1016/j.cobeha.2020.02.010
Measured on: Mixed: adults and adolescents in the included studies; Australia, Canada, US, UK, Germany, Norway and others · Mixed (adolescents to adults) · Multiple · What I read: Opened at source (full text (author manuscript)), 2026-10-25 · Funding: none from gambling industry (authors' conflict-of-interest statement) - Botha, F. (2025-03-12). The rate of sports betting has surged more than 57%, and younger people are betting more. The Conversation. doi 10.64628/AA.hh3ywmsqr (author's analysis of HILDA 2015, 2018, 2022)
Measured on: Australians in the HILDA household panel (~17,000 followed yearly); analysis sample size not reported · All adult men; band 18-44 reported as a range · Australia · What I read: Opened at source (full text (author's article)), 2026-10-18 · Funding: not reported for this analysis; author affiliated with ARC Centre of Excellence for Children and Families over the Life Course - GambleAware (2021-06-04). GambleAware publishes Safer Gambling Campaign Bet Regret summary (announcement PDF)
Measured on: The campaign's target audience in an Ipsos MORI tracking study (younger male sports bettors, per the campaign aim); sample size and ages not stated in this document · not reported in this document · Great Britain · What I read: Opened at source (full text (announcement PDF)), 2026-10-18 · Funding: Campaign funded by additional donations under a commitment by the broadcasting, advertising and GAMBLING INDUSTRIES (operator funding) - Tomei, A., Bieri, M., Porchet, C. & Simon, O. (2026). Gambling progression in young adults following online casino-gambling legalization in Switzerland. J. Gambling Studies 42(1):249-263. doi 10.1007/s10899-025-10435-6
Measured on: Same as N-088; 2019 comparison from Tomei et al. 2022 · 18-24 in 2023/24; the 2019 survey did not include 23-24-year-olds · Switzerland · What I read: Opened at source (full text), 2026-10-15 · Funding: Programme intercantonal de lutte contre la dependance au jeu (PILDJ); authors declare no conflict of interest; no operator funding reported - McGrane, E., Wardle, H., Clowes, M., Blank, L., Pryce, R., Field, M., Sharpe, C. & Goyder, C. (2023). What is the evidence that advertising policies could have an impact on gambling-related harms? A systematic umbrella review of the literature. Public Health 215, 124-130
Measured on: Whatever the 74 underlying studies sampled; not a young-men review · includes children and young people · multiple · What I read: Opened at source (full text), 2026-10-25 · Funding: Greater London Authority - McGrane, E. (2025). What is the impact of restrictions on television gambling advertising during live sport? An investigation using quasi-experimental and econometric methods. PhD thesis, University of Sheffield
Measured on: UK television advertising and scheduling data · United Kingdom · What I read: Opened at source (full thesis), 2026-10-25 - Common Sense Media (2026). Betting on Boys: Understanding Gambling Among Adolescent Boys
Measured on: US boys · 11-17 · USA · What I read: Opened at source (full report PDF), 2026-10-12 · Funding: Bezos Family Foundation; Jennifer Caldwell and John H.N. Fisher (underwriting named in the full report, not on the press release). Data collection by SSRS. No gambling-industry funding named. - Aonso-Diego, G., Garcia-Perez, A. & Krotter, A. (2025). Impact of Spanish gambling regulations on online gambling behavior and marketing strategies. Harm Reduction Journal 22:107. doi 10.1186/s12954-025-01219-7
Measured on: The whole Spanish licensed online gambling market (77 licensed operators) · not separable · Spain · What I read: Opened at source (full text), 2026-10-20 · Funding: Predoctoral grant, Government of the Principality of Asturias; postdoctoral grant, Ministry of Science, Innovation and Universities; funders had no role; no competing interests. Data: DGOJ (government regulator), publicly available on its website. - Botella-Guijarro, A., Lloret-Irles, D., Segura-Heras, J.V., Cabrera-Perona, V. & Moriano, J.A. (2020). A Longitudinal Analysis of Gambling Predictors among Adolescents. Int. J. Environmental Research and Public Health 17(24):9266. doi 10.3390/ijerph17249266
Measured on: Paired T1-T2 sample of 1,074 students from 15 high schools in Alicante; 55.12% girls · 13-18 at T1 (mean 15.06) · Spain · What I read: Opened at source (full text), 2026-10-25 · Funding: Not recorded on the record; ethics approval Miguel Hernandez University DPS.DLI.01.17 - Rockloff, M., Browne, M., Hing, N., Russell, A.M.T., Rawat, V. & Newall, P. (2026). Direct gambling marketing, direct harm: A randomised experiment. Addiction 121(7):1907-1919. doi 10.1111/add.70369
Measured on: 227 regular Australian sports and race bettors from online panels who agreed in principle to opt out (96 opt-out, 131 control); 61.7% men; 52.0% PGSI moderate-risk or problem · adults, mean 45; no age or sex subgroup analysis · Australia · What I read: Opened at source (full text), 2026-10-25 · Funding: Gambling Research Australia, funded by Commonwealth, State and Territory Governments; GRA had no role in analysis. Authors declare no conflicts for this article. - Hollenbeck, B., Larsen, P. & Proserpio, D. (2024). The Financial Consequences of Legalized Sports Gambling. Working paper, UCLA Anderson / USC Marshall, October 2024
Measured on: Representative panel of roughly 7 million US consumers (UC Consumer Credit Panel) · all adults; not split by age or sex in the sections read · USA · What I read: Opened at source (full text (PDF)), 2026-10-20 · Funding: Briskin Fund; data access via California Policy Lab (UC Consumer Credit Panel). No gambling-industry funding named. - Dasgupta, K. & Ghimire, K. (2026). Gambling and Substance Use: Early Evidence From Sports Betting Laws. Health Economics 35(6):851-865. doi 10.1002/hec.70088
Measured on: US adults in BRFSS 2016-2023; young-men subsample 286,741 observations · 35 and under, banded 18-24 / 25-29 / 30-34 · USA · What I read: Opened at source (full text), 2026-10-20 · Funding: 'The authors have nothing to report.' No conflicts declared. Lead author at the Federal Reserve Board; views stated as not the Board's.
What I couldn’t verify
- “Exposure to betting advertising encourages earlier initiation of sports betting”: Not opened, and the causal wording is the kind the field's own best review says has not been earned. That review describes an absence of longitudinal and experimental studies.
- “35% of young men gamble daily or weekly, and 58% of UK boys aged 11-16 see gambling ads at least once a week on social media”: Not opened. A second search engine produced these while this entry was being edited, and neither figure matches anything in the sources I had already opened. They may well be real. They are not checked, so they are not used.
Corrections
None yet. If something here changes, the change is logged here with its date.
How this was made
This series is researched with an AI desk: it searches, reads, pulls the numbers and drafts, including this entry. I edit and decide what is published.
How I check sources · The research library
If gambling is costing you or someone close to you more than it should, the US National Problem Gambling Helpline is free and confidential: call or text 1-800-MY-RESET (1-800-697-3738), or chat at ncpgambling.org.