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. Author manuscript; available in PMC: 2026 Nov 5.
Published before final editing as: Int Gambl Stud. 2026 Jul 3:10.1080/14459795.2026.2697457. doi: 10.1080/14459795.2026.2697457

Exploring the Relationship Between Positive Play and Perceived Knowledge in Traditional Sports Gambling

Alexander J Connolly 1, Rakin Faruk 2, Jennifer T Grant Weainandy 3, Lindsay M Stall 4, Shane W Kraus 5, Joshua B Grubbs 6
PMCID: PMC13733637  NIHMSID: NIHMS2218102  PMID: 42985329

Abstract

The growing accessibility of gambling in the United States, fueled by legalization and online platforms, has increased the need to examine both problem and responsible gambling, particularly among sports gamblers. Sports betting is often viewed as a skill-based activity, in which knowledge and skill are perceived to influence betting outcomes. The present research examines how perceived sports gambling knowledge relates to Positive Play behaviors and beliefs, as measured by the Positive Play Scale (PPS), which assesses responsible gambling from the gambler’s perspective. Participants (n = 396; Mage = 51.3, SD = 14.4, 69.9% men) were sports gamblers drawn from the fourth wave of a longitudinal sample recruited via YouGov Opinion in the United States. They rated their perceived knowledge of sports they had bet on in the past six months, reporting high confidence in their knowledge of the sport they identified as their preferred choice. Greater perceived knowledge was associated with lower Positive Play, particularly with poorer Gambling Literacy. These findings suggest that heightened confidence in one’s sports gambling knowledge may reflect overconfidence rather than accurate understanding, which may undermine responsible gambling practices. These results highlight the importance of addressing overconfidence in interventions aimed at promoting safer sports gambling behaviors.

Keywords: Responsible Gambling, Perceived Knowledge, Sports Betting, Positive Play

Introduction

Over the past decade in the United States (U.S.), the rise of online sportsbooks, casinos and betting technologies has coincided with legislative changes that have increased gambling involvement (Blaszczynski et al., 2016; Derevensky, 2016; Hayer et al., 2018; Lawn et al., 2020; Leppink et al., 2016; Ukhova & Volberg, 2025). Such increases have been most notable for sports gambling, which is now legal in 38 states, leading to increased concern about problem gambling among sports gamblers (Winters & Derevensky, 2020; Yeola et al., 2025). However, concerns about sports betting extend beyond problem gambling, and many aspects of sports betting remain poorly understood. One such domain is the role that knowledge—or more accurately, perceived knowledge— plays in the process of sports betting and related gambling problems. The present work seeks to address this area of research need. Importantly, much of the foundational research on sports gambling behavior predates the rapid expansion of legalized online sports betting in the United States following 2018, which has substantially altered the accessibility, marketing, and informational environment surrounding sports wagering.

Problem Gambling

Gambling Disorder (GD) is a psychiatric diagnosis characterized by a maladaptive pattern of gambling behavior (Ferrara et al., 2018), preoccupation with gambling (Potenza et al., 2019), and clinically significant impairment or distress (Weinstock & Rash, 2014). GD is diagnosed when individuals meet a threshold number of symptoms reflecting persistent and recurrent maladaptive gambling behavior (American Psychiatric Association, 2013). While most individuals who gamble do not develop problems, some experience problem gambling symptoms (i.e., some symptoms of gambling disorders that clearly indicate gambling-related harm despite not meeting full diagnostic criteria for GD; American Psychiatric Association, 2013; Ferris & Wynne, 2001; Weinstock et al., 2017) and a subset of those who do develop problems experience problems so severe that they meet criteria for a GD diagnosis (Marmet et al., 2021; Moore & Ohtsuka, 1999; Tabri et al., 2020). GD is debilitating (Fong, 2005), associated with poor intrapersonal (Van Patten et al., 2018), interpersonal (Marmet et al., 2021), and societal outcomes (Ladouceur et al., 1994), and linked to higher rates of mental health problems, comorbidities, and stressors (Blaszczynski et al., 2016; Elton-Marshall et al., 2016; Grubbs & Kraus, 2023a; Loo et al., 2019).

Given these costs, there is a need to understand risk and protective factors for GD and problem gambling (i.e., experiencing one to three symptoms but not meeting full diagnostic criteria for GD). Several risk factors for problem gambling have been identified, such as impulsivity (Estevez et al., 2015; Grubbs et al., 2024), greater breadth of gambling involvement (Grubbs & Kraus, 2023b; Nelson et al., 2018), greater depth of participation in gambling (Deitch, 2023), psychiatric comorbidities (Håkansson et al., 2017), substance use (Winters & Derevensky, 2020), the types of gambling with which people are engaging (Nelson et al., 2018), and traumatic brain injury history (Turner et al., 2019). In many cases, these risk factors are stable within individuals. That is, psychiatric comorbidities, traumatic brain injuries, and dispositional characteristics are general risk factors for a range of concerning outcomes. However, these stable predictors of problems are not easily targeted to prevent or treat gambling-related harms. Accordingly, there is a growing need to identify risks for and vulnerabilities to problem gambling behavior that may be more modifiable. Understanding these modifiable factors is essential for developing effective interventions.

Responsible Gambling and Positive Play

In an effort to address the growing need for frameworks that tackle dynamic gambling risk factors, responsible gambling, or policies and practices designed to prevent and reduce potential harms associated with gambling (Wood & Griffiths, 2015), has received attention from both gambling researchers (Blaszczynski et al., 2011) and operators alike (Louderback et al., 2022). A recent application of responsible gambling is Positive Play, which is a novel term referencing what has previously been referred to more generally as responsible gambling and harm reduction (Wood et al., 2017). Positive Play is thought to consist of four distinct domains, including Personal Responsibility, Gambling Literacy, Pre-Commitment, and Honesty/Control (Wood et al., 2017). Personal Responsibility assesses the extent to which gamblers accept responsibility for the time and money they spend gambling. Gambling Literacy assesses the extent to which gamblers demonstrate an accurate understanding about their odds and chances of winning. The Honesty and Control factor assesses the extent to which gamblers are in control of their gambling behaviors and how open they are about the amount of money and time they spend gambling. Finally, Pre-Commitment assesses the extent to which gamblers consider how much time and money they should spend prior to gambling (Tabri et al., 2020).

Recent research has found that these domains of Positive Play are related to specific sports wagering behaviors (Connolly et al., 2024). Gambling Literacy, or the extent to which gamblers hold an accurate understanding of their chances of winning (Tabri et al., 2020), is especially low among sports gamblers, and is associated with riskier and more niche sports gambling behaviors (Connolly et al., 2024). Positive Play research suggests that sports gamblers have notably low levels of Gambling Literacy, raising questions about the relationship between sports gambling and perceived knowledge of gambling and sports.

Similarly, erroneous perceptions and cognitive distortions have been linked to problem gambling (Aonso-Diego et al., 2026; Toneatto et al., 1997). These distortions (e.g., illusions of control, gambler’s fallacy, and overconfidence), can lead individuals to attribute outcomes to skill rather than luck (Aonso-Diego et al., 2026; Fortune & Goodie, 2012). In sports betting specifically, gamblers often believe that their expertise or knowledge about teams and players improves their chances of success (Bjerg, 2010; Bunn et al., 2020; Forrest & Pérez, 2015; Lopez-Gonzalez et al., 2019). Yet, such perceptions may in reflect cognitive bias rather than actual predictive skill (Lopez-Gonzalez et al., 2020). These distortions reflect deficits in Gambling Literacy, particularly misunderstandings of randomness, odds, and the limits of skill-based influences, which may in turn reduce engagement in responsible gambling practices (Cowie et al., 2017). As such, the interplay between perceived knowledge and erroneous perceptions is particularly important to examine, given that both may undermine responsible gambling and foster greater vulnerability to gambling-related problems.

Perceived Knowledge of Sports

Most prior research on gambling preferences has separated gambling into two different types: strategic and non-strategic (Grubbs et al., 2023; Jiménez-Murcia et al., 2020). Strategic games are defined as games that have an outcome that can be influenced by the gambler’s own knowledge or skills, while non-strategic games are defined as ones in which the gambler’s experience cannot influence the outcome of the game (Challet-Bouju et al., 2015; Connolly et al., 2025). Sports gambling is generally categorized as a form of strategic gambling, which may be rooted in the perception that sports gambling success can be influenced by the skills and knowledge of the bettor (Browne et al., 2015). Sports gamblers often believe they are knowledgeable about sports and contend that higher sports knowledge will lead to more gambling success (Winters & Derevensky, 2020). This belief may be reinforced by advertisements that emphasize knowledge as key to winning (Killick & Griffiths, 2023). Moreover, there is evidence that sports gamblers who are targeted by advertisements tend to be highly competitive, want to win, and thus wish to utilize their sport knowledge and expertise in a favorable way (Kwak et al., 2010; Newall, 2025).

Sports gamblers may be uniquely motivated by a desire to gain and display sports knowledge (Gökce Yüce et al., 2022). Sports gamblers are likely to be motivated to search for information about teams and players, thinking that it will increase their odds of winning (Cooper et al., 2022), and skill-based gamblers overestimate their personal ability to win (Mercier et al., 2018). Perceived knowledge of sports may reflect both motivation and interest in strategic gambling, and lead to increased gambling participation (Kim & Lopez, 2025; Lee et al., 2011).

Perceived knowledge of sports has a direct effect on inflating winning confidence in traditional fantasy sports (Lee et al., 2011), and both sports gamblers and fantasy sports players have increased confidence levels in their own abilities as competitors (Kwak et al., 2010). While traditional sports gamblers demonstrate higher rates of gambling problems than fantasy sports gamblers (Grubbs & Kraus, 2023b), both report believing that success is impacted by self-reported sports knowledge (Kwak et al., 2010). However, self-reported knowledge might be a poor proxy for one’s actual expertise regarding sports or success in sports gambling. Measures of self-reported or self-rated knowledge often indicate inflated self-confidence in one’s own knowledge levels, rather than objective knowledge (Brucks, 1985). For example, in studies of other purportedly skill-based forms of gambling (e.g., track betting), those who perceived themselves to be experts did no better than chance in their betting (Browne et al., 2015). People who engage in sports gambling often overestimate the control they have in determining outcomes and have inordinately high confidence in their likelihood of winning, despite other crucial factors being involved (Kwak et al., 2010). Another study found that expert hockey betters did not make better predictions than random selections (Cantinotti, 2003), suggesting that perceived knowledge of sports may lead to increased gambling behavior through cognitive distortions. In other words, this inflated perception of sports wagering knowledge may be associated with greater risk taking, reflected in lower endorsement of responsible gambling beliefs, consistent with erroneous beliefs about the role of sports knowledge in determining betting outcomes (Deitch, 2023).

Present Study

As elaborated above, the role that perceived knowledge about sports betting plays in gambling behavior and problem gambling symptoms among sports gamblers is an area requiring further investigation. The present study primarily examined associations between perceived sports gambling knowledge and Positive Play, while also exploring whether perceived knowledge was related to problem gambling symptoms. In the present study, problem gambling symptoms refer to behavioral indicators of gambling-related harm, as assessed using the Problem Gambling Severity Index (PGSI). Accordingly, the present study examines associations between perceived sports gambling knowledge and two key outcomes: (a) responsible gambling behaviors and beliefs, as indexed by Positive Play, and (b) problem gambling symptoms.

While the purpose of the present study was largely exploratory, we did generally expect that higher reported levels of perceived knowledge about sports betting would be associated with greater problem gambling symptoms. Given that higher perceived gambling knowledge has been associated with greater problem gambling symptoms (Hanss et al., 2015), and that sports gamblers are known to perceive themselves as highly knowledgeable about sports (Cantinotti, 2003), an association between self-reported knowledge of sports betting itself should demonstrate similar associations with problem gambling symptoms.

Additionally, we aimed to explore associations between positive play and perceived knowledge within traditional sports gambling. While the present study seeks to identify trends in both directions of correlation via exploratory research, there was an expectation that higher perceived sports knowledge will correlate with less Positive Play. As previous research suggests that higher perceived gambling knowledge is related to higher problem gambling behaviors due to overestimation of control (Barrault & Varescon, 2012), it is reasonable to suggest that this may be the inverse for responsible gambling behaviors and beliefs. As such, understanding the relationships between perceived sports gambling knowledge and both problem gambling and Positive Play will allow operators, clinicians, and researchers to more effectively design prevention and intervention programs.

For the purposes of this paper, when we refer to Positive Play we are only referring to the PPS and its subscales (Wood et al., 2017, 2024), whereas when we use the term responsible gambling we are referring to the broader constellation of responsible behaviors. We acknowledge the larger criticisms and controversy surrounding this topic (Shaffer et al., 2023), and for the purposes of this paper use Positive Play as a specific, branded subset of responsible gambling. In addition, in the present study, the term “problem gambling symptoms” is used to refer to severity along this continuum, as indexed by the Problem Gambling Severity Index (PGSI; Ferris & Wynne, 2001).

Methods

Participants and Procedure

The sample for this study came from a longitudinal dataset (total baseline N = 4,363) with multiple waves, consisting of a census-matched sample of American adults (n = 2,806) and an oversample of sports-gambling adults in the U.S. (n = 1,557) (Grubbs & Kraus, 2022), recruited via YouGov. Importantly, YouGov has shown clear utility as a means for studying gambling behavior at the population level (Wardle & McManus, 2021) and analyses show that—though it often overestimates problem gambling rates and should not be used to draw epidemiological conclusions—it is very useful in understanding gambling behavior more broadly in the population (Grubbs & Kraus, 2022, 2024; Sturgis & Kuha, 2022).

Five waves of data were collected from March of 2022 to March 2024. The current study used data from the fourth wave of data collection, in Fall of 2023 (n = 1977). In previous waves, participants were not asked their perceived knowledge of particular sports, nor their favorite sports to bet on, which were key variables for the current study. In the present study, we included participants who reported betting on at least one sport in the past six months. Only traditional sports gamblers were included (i.e., those who reported betting on sporting events as opposed to those who only engaged in fantasy sport play or daily fantasy sport play) because they were the participants in the larger sample who received the perceived sports knowledge questions, resulting in a final sample of n = 396 (Mage = 51.3, SD = 14.4; 277 men (69.9%); 289 identified as White (73.0%), see Supplemental Table 1 for full demographic data). Ethics were provided by University of New Mexico Institutional Review Board “Sports Wagering in the United States,” 2310086775.

Measures

Positive Play Scale (PPS)

We assessed responsible gambling behaviors and beliefs using the Positive Play Scale (Wood et al., 2017). The Positive Play Scale is a 14-question scale divided into four subscales, including Personal Responsibility (i.e., “I should be able to walk away from gambling at any time”; four questions, Cronbach’s alpha = .870), Gambling Literacy (i.e., “Gambling is not a good way to make money”; three questions, Cronbach’s alpha = .726), Pre-Commitment (i.e., “I considered the amount of MONEY I was willing to spend BEFORE I gambled”; four questions, Cronbach’s Alpha = .811), and Honesty and Control (i.e., “I was honest with my family and/or friends about the amount of MONEY I spent gambling”; three questions; Cronbach’s Alpha = .868). The Pre-Commitment and Honesty and Control items were anchored at 1 (never) and 7 (always) while the Personal Responsibility and Gambling Literacy items were anchored at 1 (strongly disagree) and 7 (strongly agree). Responses were averaged across all items for each subscale.

Problem Gambling Severity Index

We assessed problem gambling symptoms using the Problem Gambling Severity Index (PGSI; Ferris & Wynne, 2001). The PGSI is a nine-question scale (i.e., “When you gambled, did you go back another day to win back what you have lost”) designed to categorically place people into various categories of problem gambling risk (i.e., non-problems, low risk, moderate risk, and high risk). The questions are anchored at 0 (never) and 3 (almost always). Responses were summed to create a problem gambling severity score. Cronbach’s alpha was .959, indicating high reliability.

Sports Betting Perceived Knowledge

To measure participants’ levels of perceived gambling knowledge, they were first asked what sports they bet on from a list of 11 possible options (i.e., “Basketball”, “American Football”, etc.; see Table 1). They were then asked to identify their favorite sport to bet on, the sport they bet on most often, and the sport they were most successful betting on. Then, for each sport that was endorsed, participants were asked the extent to which their knowledge about the sport was below average, average, or above average in comparison to other people who also gamble on that sport (i.e., “That is, when you think about how much you know about the sports you’ve bet on, how would you rate that knowledge in comparison to other people making bets?”). This was assessed through a seven-point Likert scale from 1 (Far Below Average) to 7 (Far Above Average).

Table 1.

Descriptive Statistics for Sports Knowledge

Variable Count (n) endorsing each sport at their:
Total Endorsing Betting on Specific Sport N M SD Most Often Favorite Most Successful
Basketball 208 4.66 1.37 39 44 47
Baseball 190 4.72 1.34 39 33 30
Soccer 106 4.51 1.68 33 34 37
Football 345 4.92 1.35 232 231 221
Hockey 96 4.61 1.53 6 9 14
Golf 62 4.27 1.52 3 2 4
E-Sports 66 4.70 1.40 8 7 6
Tennis 51 4.49 1.58 3 1 4
Motor 55 4.82 1.65 4 6 7
Boxing 91 4.86 1.47 9 11 7
MMA 80 4.60 1.67 13 12 12
Often 304 5.27 1.32 - - -
Favorite 304 5.27 1.32 - - -
Success 304 5.24 1.33 - - -
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Note. Mixed martial arts (MMA)

We created composite indices reflecting perceived knowledge of participants’ favorite, most frequent, and most successful sports to bet on. For each participant, the perceived knowledge rating for their selected sport in each category was assigned to the corresponding index. For example, if a participant identified basketball as their favorite sport to bet on and had rated their perceived basketball knowledge as a 6, their ‘favorite sport perceived knowledge’ score was recorded as a 6. Given the high correlation between these three variables (favorite, most often, most successful) and little differentiation between these three ratings in associations with other variables, we aggregated the three into a single average. We will be referring to this mean score as Preferred Sport Perceived Knowledge (PSPK) throughout the rest of this paper. For individuals who only bet on one sport, perceived knowledge for that sport was assigned as their favorite, most often, and most successful sport, allowing inclusion of all sports bettors in analyses (N = 396).

Gambling Depth and Breadth

We calculated gambling depth and breadth through two questions. First, we calculated gambling depth, referred to as gambling frequency, by asking participants on a scale from 1 (Never) to 6 (More than once per day) how often they typically gambled on sports. In addition, we asked them to choose from a list of 14 different types of gambling behaviors (e.g., slots, lottery play, sports gambling) which gambling activities they had engaged in over the past six months. A simple count variable was computed based on the number of different gambling activities they endorsed.

Data Analysis

We conducted a series of analyses in IBM SPSS statistics version 31.0. First, descriptive statistics were computed, including mean knowledge scores for each sport (i.e., average perceived knowledge of each sport listed). To determine participants’ levels of perceived knowledge for their preferred sport(s) to bet on, we calculated their individual knowledge scores for the sport they mentioned as their favorite to bet on, bet on most often, and most successful when betting. Likewise, to test how their perceived knowledge of their preferred sports related to their Positive Play scores, we conducted hierarchical regressions, predicting Gambling Literacy, Personal Responsibility, Honesty and Control, Pre-Commitment, and PGSI scores. We analyzed the subscales of the PPS separately as Positive Play is conceptualized as a multidimensional construct, with each domain reflecting distinct aspects of responsible gambling (Tabri et al., 2020; Wood et al., 2017). In each case, age, gender, gambling breadth, and gambling frequency were entered in Step 1, with perceived knowledge of their often/favorite/successful sport entered in Step 2.

Given prior research demonstrating heterogeneity in sports gambling (Connolly et al., 2024), and evidence that gambling breadth is associated to more severe gambling problems (LaPlante et al., 2014), we examined whether individuals who bet on multiple sports differed from those who bet on a single sport across indicators of gambling involvement and perceived knowledge. Independent samples t-tests were conducted to compare individuals who reported betting on only one sport versus those who reported betting on multiple sports with respect to perceived knowledge of their preferred sport, gambling breadth, gambling frequency, age, and problem gambling symptoms. These analyses were conducted to descriptively examine group differences in gambling involvement and perceived knowledge. Holm corrections were applied to all p-values to account for multiple comparisons and reduce Type I error.

Hierarchical regression was used to examine whether perceived sports gambling knowledge accounted for unique variance in Positive Play and problem gambling symptoms beyond demographic characteristics and gambling involvement. The ordering of predictors reflects a conceptual distinction between relatively stable background characteristics (e.g., age, gender) and gambling involvement indicators (e.g., breadth and frequency), versus perceived knowledge, rather than any assumption of temporal or causal precedence. Importantly, the structure of the models should be interpreted as a statistical control strategy rather than evidence of causal ordering. Given the limited prior research on perceived sports gambling knowledge, several analyses were conducted in an exploratory manner to characterize patterns of association and generate hypotheses for future research, rather than to test strictly preregistered hypotheses.

The authors declare no conflict of interest regarding this work and attest that no portion of this work has been previously disseminated or published elsewhere. Ethical approval for this project has been granted via the Institutional Review Board of the University of the corresponding author.

Results

To better understand the relationship between perceived sports gambling knowledge and Positive Play, we investigated perceived knowledge in three ways: 1) mean knowledge scores across sport, 2) mean knowledge scores for PSPK, and 3) how knowledge relates to Positive Play and problem gambling symptoms. Full correlations of relevant study variables are available in Table 2.

Table 2.

Pearson correlations between Positive Play subscales and perceived knowledge of various sports

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
1 PR -
2 GL .45** -
3 Pre-C .52** .31** -
4 H & C .45** .31** .76** -
5 PGSI −.47** −.51** −.43** −.41** -
6 Basketball −.06 −.19** .03 .06 .06 -
7 Baseball .01 −.16* .02 .04 .03 .63** -
8 Soccer −.20* −.40** −.20* −.16 .13 .45** .44** -
9 Football −.02 −.19** −.05 −.03 .10 .60** .56** .38** -
10 Hockey .01 −.23* −.02 .05 .11 .58** .59** .54** .54** -
11 Golf .01 −.12 −.07 .00 .13 .40** .52** .59** .33* .65** -
12 Esports −.03 −.10 .12 .33** .01 .48** .71** .64** .62** .65** .72** -
13 Tennis −.02 −.26 .23 .27 −.22 .80** .60** .72** .74** .78** .69** .76** -
14 Motor .03 −.04 .09 .30* −.17 .63** .70** .74** .53** .71** .57** .84** .90** -
15 Boxing −.16 −.19 .03 .11 .14 .69** .64** .50** .49** .66** .55** .61** .85** .79** -
16 MMA −.21 −.19 .00 .11 .10 .72** .65** .61** .48** .71** .60** .72** .76** .79** .84** -
17 Often .03 −.24** −.06 −.06 .03 .58** .67** .53** .80** .53** .43** .58** .64** .58** .52** .58** -
18 Favorite .05 −.21** −.02 −.04 .02 .63** .69** .57** .74** .57** .38** .48** .56** .65** .51** .58** .90** -
19 Success .01 −.22** −.02 .01 −.01 .60** .68** .56** .75** .54** .37** .54** .63** .70** .54** .61** .90** .88** -
20 Age .21** .35** .15** .11* −.19** −.05 .13 −.29** .02 −.10 .04 −.30* −.07 .05 −.17 −.27* −.02 −01 −.03
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Note.

*

p <.05,

**

p < .01.

PR = Personal Responsibility. GL = Gambling Literacy, Pre-C = Pre-commitment, H & C = Honesty and Control, PGSI = Problem Gambling Severity Index.

Mean Knowledge Scores

Participants endorsed gambling on all listed sports, though endorsement frequency varied. The sports that were the most frequently endorsed by participants were Football (n = 345; 87.12%), Basketball (n = 208; 52.53%), and Baseball (n = 190; 47.98%), and the sports least endorsed were Tennis (n = 51; 12.88%), Motorsports (n = 55; 13.89%), and Golf (n = 62; 15.66%) (see Table 1). We also examined frequencies of preferred sports. In terms of participants’ favorite, most often, and most successful sport to bet on, football was the most popular (n’s: often = 232, favorite = 231, success = 221) followed by basketball (often = 39, favorite = 44, success = 47) and baseball (often = 39, favorite = 33, success = 30; see table 1).

The mean scores of perceived knowledge for all sports ranged from 4.27 (Golf) to 4.92 (Football), corresponding to ratings between average and somewhat above average. Given the high correlation between often, favorite, and success (rO-F = .922, rO-S = .926, rF-S=.911) we calculated a mean variable, PSPK, taking the average of the three values (α = .972).

Responsible and Problem Gambling

To test how perceived sports knowledge predicted Positive Play and problem gambling symptoms, we conducted five hierarchical regressions predicting the four Positive Play subscales and the Problem Gambling Severity Index. In Step 1, demographic and gambling involvement variables (age, gender, gambling frequency, and gambling breadth) were entered. In Step 2, PSPK was added to evaluate the degree to which perceived knowledge contributed unique variance to the model. We applied the Holm-adjustment to p-values from Step 2 to control for multiple comparisons and Type I error (Table 3).

Table 3.

Hierarchical regression predicting Positive Play and problem gambling indices by demographics and PSPK (Holm-adjusted p values for Step 2)

Gambling Literacy Personal Responsibility Honest and Control Pre-Commitment PGSI
Step 1
Gambling Breadth −0.31 ** −0.09 −0.021 −0.04 0.21 **
Male Gender −0.01 0.06 −0.01 0.01 −0.02
Age 0.30 ** 0.20 ** 0.11 * 0.15 ** −0.22 **
Gambling Frequency −0.20 ** −0.04 −0.16 ** −0.12 * 0.21 **
R2 0.301 ** 0.060 ** 0.039 ** 0.041 ** 0.179 **
F for R2 42.16 ** 6.25 ** 3.99 ** 4.15 ** 21.37 **
Step 2 (+ PSPK)
PSPK −0.12 * 0.06 0.00 −0.03 −0.05
Gambling Breadth −0.30 ** −0.09 −0.02 −0.03 0.21 **
Male Gender 0.01 0.05 −0.01 0.01 −0.01
Age 0.30 ** 0.20 ** 0.11 0.15 * −0.22 **
Gambling Frequency −0.18 ** −0.059 −0.16 * −0.11 0.22 **
R2 0.315 ** 0.064 ** 0.039 ** 0.041 ** 0.182 **
ΔR2 0.014 * 0.004 0.000 0.000 0.003
F for ΔR2 7.53 1.50 0.00 0.25 1.08
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Note. β values are standardized coefficients. Step 1 significance uses unadjusted p values; Step 2 significance uses Holm-adjusted p values. PSPK = composite of Often/Fav/Success knowledge.

*p* < .05 (*),

*p* < .01 (**).

For Gambling Literacy, age was a significant positive predictor, whereas both gambling breadth and gambling frequency were significant negative predictors. When PSPK was entered in Step 2, it emerged as a significant negative predictor of Gambling Literacy (β =−.12, Holm-Adjusted p = .030), such that greater knowledge of one’s preferred sport was associated with lower Gambling Literacy. Age, gambling breadth, and gambling frequency remained significant predictors following Holm correction. For Personal Responsibility, age showed a small positive association in Step 1 and Step 2; however, gambling breadth, gambling frequency, gender, and PSPK were nonsignificant following Holm correction.

For Honesty and Control, gambling frequency was a significant negative predictor and remained a significant negative predictor following Holm correction, whereas age was no longer significant. The addition of PSPK did not account for a significant change in explained variance. Similarly, for Pre-Commitment, age was a significant positive predictor in Step 1 and remained significant following Holm adjustment, whereas gambling frequency and PSPK did not contribute unique variance.

For PGSI, age was a significant negative predictor, whereas both gambling breadth and gambling frequency were significant positive predictors in both steps (all Holm-adjusted ps < .005). PSPK did not significantly predict PGSI nor contribute unique variance beyond demographic and gambling factors.

Comparisons Between Single-Sport and Multi-Sport Bettors

Given observed differences in perceived knowledge scores between individuals who bet on only one sport versus those who bet on multiple sports, independent samples t-tests were conducted to compare these groups. Individuals who bet on multiple sports reported significantly higher perceived knowledge of their preferred sport (Msingle = 4.47, SDsingle = 1.38, Mmultiple = 5.26, SDmultiple = 1.28), greater gambling breadth (Msingle = 4.57, SD = 2.66, Mmultiple = 6.05, Msingle = 3.32), higher gambling frequency (Msingle = 2.41, SDsingle = 1.40, Mmultiple = 3.31, SDmultiple = 1.31), and were younger on average (Msingle = 56.52, SDsingle = 14.90, Mmultiple = 49.74, SDmultiple = 13.69) than individuals who bet on a single sport (all ps < .001). Differences in problem gambling symptoms between groups were small and did not reach statistical significance. Effect sizes ranged from small to moderate, suggesting meaningful behavioral and perceptual differences between single-sport and multi-sport bettors. These findings indicate that individuals who engage in betting across multiple sports may represent a distinct subgroup of sports gamblers characterized by greater involvement and confidence in their gambling related knowledge.

Discussion

Although sports gambling is often viewed as strategic, little research has examined how knowledge relates to gambling behavior. The present research aimed to advance understanding of Positive Play behaviors/beliefs by examining how sports gamblers perceive their knowledge across a variety of sports. Although exploratory, several findings aligned with expectations. Consistent with the exploratory and correlational design of the present study, all associations should be interpreted as descriptive rather than causal. Although we discuss potential mechanisms that may underlie these relationships, such interpretations are speculative and intended to inform future longitudinal and experimental research.

First, the perceived knowledge levels for each sport were predictably high, with the mean of each being above the scale midpoint, suggesting that participants generally rated their knowledge as above average. In addition, when narrowed down just to PSPK, the scores were even higher, particularly when restricting the comparison to individuals who bet on more than one sport and therefore had multiple options from which to select. These results suggest that sports gamblers are highly confident, especially in their preferred sport. Of note, the way that perceived knowledge was measured in this study was using a comparison of other people also gambling on that same sport. Not only were sports gamblers reporting high levels of perceived knowledge of the sports they bet on, but they also believed themselves to be more knowledgeable than the majority of others who bet on the same sports. This comparative framing is consistent with recent work conceptualizing overconfidence in gambling as a social comparison bias, rather than solely as inaccurate self-estimation (Xu et al., 2024). Thus, individuals who wager on sports may be prone to overconfidence, as reflected in these high levels of perceived knowledge.

In addition, although not part of the study’s initial aims, the present research explored differences in participants’ knowledge of and preferences for betting on various sports. Unsurprisingly, football emerged as the most preferred sport, with basketball, baseball, and soccer clustered closely behind. This aligns with general trends in American sports fandom, where football is the most popular sport and also the most heavily promoted in gambling advertisements. Notably, average perceived knowledge scores varied by sport, with football rated the highest and golf the lowest. Given small and uneven sample sizes for many of the individual sports, it is difficult to draw firm conclusions. However, these findings may have implications for how betting on certain sports, particularly football, are targeted by advertisements. To the extent that higher perceived gambling knowledge reflects overconfidence, elevated perceived knowledge scores among football bettors may warrant further attention, particularly in light of evidence linking overconfidence to more favorable gambling attitudes and increased engagement (Philander & Gainsbury, 2021), even though sport-specific risk for problem gambling was not directly examined in the present study.

Gambling Literacy, or the extent to which bettors hold an accurate understanding about their odds and chances of winning (Tabri et al., 2020), was negatively associated with PSPK. This finding is, in some senses, paradoxical, as Gambling Literacy is intended to measure how much one truly understands, or knows, about how gambling works. This pattern aligns with more recent work conceptualizing overconfidence in gambling as a social-comparative and motivational bias, rather than a simple miscalibration of objective knowledge. In this framework, perceived expertise and confidence are linked to more favorable gambling attitudes and greater engagement, even in the absence of corresponding objective skill (Philander & Gainsbury, 2020; Xu et al., 2024). Yet, in this study lower Gambling Literacy was associated with higher perceived gambling knowledge. This pattern suggests a disconnect between confidence in one’s sports gambling knowledge and understanding of how gambling operates more generally. These findings suggest that perceived knowledge of sports may be associated with gambling behavior through cognitive distortions. This interpretation aligns with previous literature in the realm of judgement and decision making which has examined related concepts such as overconfidence and unjustified confidence and their links to various negative outcomes (Parker & Stone, 2014; Philander & Gainsbury, 2020; Xu et al., 2024). Given this, it is reasonable to infer that this overconfidence observed in decision making research may also be relevant to individuals who gamble on sports.

Beyond Gambling Literacy, perceived knowledge was not uniquely associated with other aspects of Positive Play after accounting for demographic and gambling involvement variables. The reasons for these null findings are unclear, and given the statistical framework in which we conducted this (e.g., frequentist approaches), we urge caution in interpreting this absence of evidence (i.e., nonsignificant associations in regression models) as evidence of absence (i.e., the assertion that perceived knowledge is wholly unrelated to Personal Responsibility, Honesty and Control, or Pre-Commitment). Even so, the absence of substantive relationships here does suggest that there are weak links between perceived knowledge and these other aspects of Positive Play. This pattern may simply reflect the behavioral and attitudinal aspects of these domains of Positive Play. More simply, Honesty and Control, Personal Responsibility, and Pre-Commitment reflect attitudes toward gambling and behaviors regarding gambling, rather than cognitive beliefs about gambling. Ultimately, the observed pattern of associations is consistent with Gambling Literacy, instead of the other Positive Play pathways, playing a central role in how perceived knowledge relates to responsible gambling beliefs, with greater literacy being associated with lower confidence in one’s own sports gambling knowledge.

In addition, we tested problem gambling symptoms. Gambling breadth and frequency emerged as consistent positive predictors, indicating that behavioral intensity and variety contribute more strongly to problem gambling than demographics. It is important to note that Positive Play subscales were not included as predictors in these models, as the aim of the regression analyses was to examine whether perceived sports gambling knowledge accounted for unique variance in problem gambling severity beyond demographic and gambling involvement factors. Notably, although one of the stated aims of the present work was to examine whether perceived sports gambling knowledge was related to problem gambling symptoms, no such association was observed. This null finding warrants attention, as prior research has often emphasized the role of erroneous beliefs and overconfidence in the development of gambling problems (Aonso-Diego et al., 2026; Clark, 2010; Fortune & Goodie, 2012; Philander & Gainsbury, 2020). More recent work suggests that overconfidence may be indirectly associated with gambling through pathways involving gambling attitudes, motivations, or engagement patterns rather than direct symptom severity (Philander & Gainsbury, 2020; Xu et al., 2024). One possible explanation is that the association between perceived knowledge on problem gambling may be indirect, operating through other mechanisms such as gambling frequency or Gambling Literacy, rather than directly predicting problem gambling symptoms. Another possibility is that perceived knowledge is more strongly associated with problem gambling only among individuals with higher levels of gambling involvement or other vulnerabilities. Future research using longitudinal and more nuanced measures of both perceived and objective knowledge may help clarify these dynamics.

In addition, age was also examined due to previous research demonstrating associations between age and problem gambling (Grubbs & Kraus, 2022). Age was related to several Positive Play variables, typically in a positive direction. However, we did not find evidence of a relationship between age and perceived knowledge. Accordingly, though it appears that age is not related to perceive knowledge, it may be related to Positive Play behaviors/beliefs such that younger individuals may be less likely to endorse these. Of note, the mean age of this sample of sports gamblers (n = 51.3) is higher than we typically see in similar samples. This may be attributable to the data collection procedures (i.e., census-matched data with a minimum age of 21 by the time of analyses). This may reflect an inability to generalize our findings to a more typical sports gambling sample, such as younger males.

Limitations and Implications

Despite the novel findings noted in the study, the results are not without their limitations. First, this was a cross-sectional study which relied on self-report for all its variables. Although hierarchical regression models were used to examine incremental associations between variables, the cross-sectional nature of the data precludes conclusions regarding temporal or causal ordering. In addition, the primary construct of interest, sports gambling knowledge, was assessed via self-report and therefore reflects perceived rather than objective knowledge. To this point, no objective measure of sports gambling knowledge has been created, meaning that we must rely on perceived knowledge measures for the time being. Future studies could investigate how to objectively measure one’s sports gambling knowledge.

In addition, the present research measures perceived knowledge of sports gambling by using a particular comparison group—other sports gamblers. While these results are still salient, future studies could investigate how perceived knowledge changes when other comparison groups are used. For example, this study asked each individual to compare against other individuals betting on sports, but the results may differ if the comparison groups were other sports fans, or just the average American in general. Furthermore, given that much of the broader literature used to contextualize these findings was developed prior to the expansion of legalized online sports betting in the United States, existing frameworks may not fully capture the current accessibility, marketing landscape, and informational environment surrounding modern sports wagering.

Finally, perceived sports gambling knowledge was measured across individual sports. This was necessary for our design, as one’s perceived degree of knowledge for a particular sport may differ with another sport; and a mean variable (i.e., perceived knowledge across all sports bet on) would not be able to capture the true perceived sports gambling knowledge score of an individual. However, this reduces our ability to infer whether overconfidence is a person specific phenomenon or if it is more isolated to specific sports. Future studies could further investigate the differences between sports as well as asking individuals what their overall sports gambling knowledge is relative to others who engage in sports gambling.

Despite these limitations, the present study offers some implications for future research and policy. First, while there has been a growing push to promote responsible behaviors and beliefs among sports gamblers, the current research suggests alternative pathways to help us understand problem gambling—specifically, the ability to accurately appraise one’s knowledge. Given the emphasis in sports gambling advertising on the idea that sports gambling success is tied to expertise and loyalty to a team (Killick & Griffiths, 2023), there is a clear need to examine the actual role of perceived versus objective knowledge. Because the present study only measured perceived knowledge, or individuals’ subjective confidence in their knowledge of sports, it will be important for future research to examine how problem and responsible gambling behaviors relate to objective knowledge, or what individuals truly understand about sports betting. The present results suggest a possible inverse relationship between perceived knowledge and Gambling Literacy. However, without reliable methods for measuring sports gambling knowledge, we cannot understand its impact on both responsible and problematic gambling. Clinicians may also benefit from considering gambling related confidence, knowledge, and cognitive distortions when assessing and treating individuals with gambling problems.

Finally, there has been an increase in responsible gambling research, particularly around the concept of Positive Play. Although previous studies have reported mixed findings regarding the relationships between different facets of Positive Play and various gambling related behaviors, the present study highlights Gambling Literacy as a particularly important area to address. Given that sports gambling advertisements often emphasize knowledge and expertise, this aspect may be especially relevant for policymakers and industry operators to consider. As Gambling Literacy appears to be one of the lower scorings of the Positive Play subscales among gamblers, efforts to improve it – through education and revised messaging about the true odds and mechanisms of gambling – could help promote more responsible and safer sports gambling behaviors.

Conclusions

The present study examined the relationship between sports gambling knowledge and Positive Play gambling behaviors/beliefs. Sports gamblers were found to be extremely confident in their own betting knowledge, even when compared to other people gambling on that same sport. Sports gamblers were especially likely to endorse high perceived knowledge levels when considering the sport that is their most preferred to bet on. Furthermore, Gambling Literacy was positively associated with age but negatively associated with gambling breadth, frequency, and perceived knowledge of one’s preferred sport. Although gambling breadth and frequency were associated with higher PGSI scores, PSPK was not significantly related to PGSI, suggesting that confidence in one’s sports knowledge may be more closely tied to Positive Play beliefs than to problem gambling symptoms. While the current work highlighted the potential relationship between sports gamblers’ perceived knowledge and Positive Play gambling behaviors/beliefs, there is much more to be done to truly understand how knowledge may be related to gambling.

Supplementary Material

1

Funding Statement:

This work was funded via grants from the International Center for Responsible Gaming, the Problem Gambling Network of Ohio, and the Kindbridge Research Institute awarded to JBG and SWK. The funders had no role in design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Biographies

Alexander Connolly, M.A.: Alex Connolly is currently a Ph.D. candidate at the University of New Mexico where he studies responsible and problem gambling under the advisement of Dr. Joshua Grubbs.

Rakin Faruk, B.S.: Rakin Faruk graduated in 2024 from the University of New Mexico and is currently an independent scholar.

Dr. Jennifer T. Grant Weinandy, Ph.D.: Dr. Grant Weinandy is an Assistant Professor at Ohio University, where she uses mixed methods to study perceptions and experiences of addictions and addiction treatment in adults.

Lindsay M. Stall, M.A.: Lindsay Stall is currently a Ph.D. student at the University of South Florida, where she studies externalizing behaviors.

Dr. Shane W. Kraus, Ph.D.: Dr. Kraus is an Associate Professor at the University of Nevada Las Vegas, where he studies psychopathology, substance use disorders, gambling disorder, and compulsive sexual behavior disorder.

Dr. Joshua B. Grubbs, Ph.D.: Dr. Grubbs is an Associate Professor at the University of New Mexico where he studies compulsive sexual behavior disorder, gambling disorder, and personality traits. In addition, Dr. Grubbs is an investigator at the Center on Alcohol, Substance use, And Addictions.

Footnotes

Disclosures: The authors report there are no competing interests to declare

Data Availability Statement:

The data that supports the findings of this study will be made publicly available in an open-access repository following publication. A link to the dataset will be provided upon acceptance.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

1
NIHMS2218102-supplement-1.pdf (201.7KB, pdf)

Data Availability Statement

The data that supports the findings of this study will be made publicly available in an open-access repository following publication. A link to the dataset will be provided upon acceptance.

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