Content uploaded by Carrie A. Shaw
Author content
All content in this area was uploaded by Carrie A. Shaw on Jan 04, 2016
Content may be subject to copyright.
Characteristics of Good Poker Players
Carrie A. Leonard
1
& Robert J. Williams
2
1
Department of Psychology, University of Lethbridge, Lethbridge, Alberta, Canada
2
Faculty of Health Sciences, University of Lethbridge, Lethbridge, Alberta, Canada
Abstract
Poker is characterized as a ‘‘ mixed’’ game: a game that includes both skill and chance
components. But what individual differences are characteristic of skilled poker
players? No previous study has sought to evaluate the full scope of characteristics
contributing to playing skill. The purpose of this study was to fill this void by
attempting to comprehensively examine the individual characteristics associated
with good poker players. Results from a sample of undergraduate students and
community members (n= 100) showed that good players are more likely to be male,
to have lower susceptibility to gambling fallacies, a greater tolerance for financial
risk, superior social information processing skills, and less openness to aesthetic and
imaginative experience. Evidence from this study also indicates that having sufficient
levels of most of these attributes is more important for poker success than having
exceptional strength in just one or two of these areas.
Keywords: poker, skill, individual differences, gambling, intelligence
Résumé
Le poker est un jeu )mixte *: il repose à la fois sur le hasard et l’habileté des joueurs.
Mais quels sont les traits personnels qui caractérisent les joueurs habiles? Aucune étude
n’a encore cherché à évaluer l’ensemble des caractéristiques qui contribuent à l’habileté
des joueurs. L’objectif de la présente recherche était donc de combler ce manque en
tentant de proposer un examen exhaustif des caractéristiques personnelles associées aux
bons joueurs de poker. Les résultats de l’étude, qui portait sur un échantillon composé
d’étudiants de premier cycle et de membres de la collectivité (n= 100), ont montré qu’un
bon joueur est plus susceptible d’être un homme, d’être peu enclin à l’illusion du joueur,
d’avoir une tolérance élevée aux risques financiers et d’être peu ouvert aux expériences
esthétiques et d’imagination. Les résultats indiquent également que pour réussir au
poker il est plus important de posséder la plupart de ces caractéristiques à un degré
suffisant que d’en posséder une ou deux à un degré exceptionnellement élevé.
44
Journal of Gambling Issues
Issue 31, November 2015 DOI: http://dx.doi.org/10.4309/jgi.2015.31.5
http://igi.camh.net/doi/pdf/10.4309/jgi.2015.31.5
Introduction
Poker is identified as a ‘‘ mixed’’ game. A mixed game is one in which the outcomes
are determined by a combination of skill and chance—regardless of the relative
contribution of either. Whether poker is predominated by skill or by chance is
debated within both the scientific and legal literature. Attention devoted to the
relative contribution of skill in poker is an important issue, as it has practical
relevance for the legal standing of the game in relation to gambling and tax revenue
laws.
As skill is a component of poker that contributes to the determination of game
outcomes, empirical investigation of the traits indicative of skilled poker players is
also justifiable. Which individual differences are characteristic of skilled poker
players? No previous study has sought to evaluate the full scope of characteristics
presupposed to contribute to playing skill. However, some previous empirical
investigations have shed light on the nature of poker players more generally.
Demographics
It is well established that poker players predominantly comprise young adult males
who report higher rates of alcohol use than do other gambling populations (i.e., non-
poker playing; Dannewitz & Weatherly, 2007; Meinz et al., 2012; Oliveira & Silva,
2001; Shead, Hodgins, & Scharf, 2008). However, beyond the research on these
general demographic characteristics, there is no literature available regarding
whether certain demographic characteristics are associated with superior poker skill.
Quantitative/Statistical Skill
As would be expected, better players have been shown to make more statistically
optimal poker-related decisions. For example, St. Germain and Tenenbaum (2011)
found that better decision-making processes, leading to higher expected value—the
average amount a player can expect to win or lose on an individual bet—were
consistently demonstrated by more expert players and that these players incorporated
more situation-relevant cues during decision making than did novice players.
Experience
1
Palomäki, Laakasuo, and Salmela (2012) concluded that more experienced players
make more mathematically justified poker decisions. Similarly, two studies by Linnet
showed that in more experienced players, probability estimation (i.e., the potential of
hands winning) was superior to that in inexperienced players (Linnet et al., 2012;
Linnet, Gebauer, Shaffer, Mouridsen, & Møller, 2010).
1
Experienced players may not be more skilled. Any inconsistencies in terminology used (e.g., good
player, experienced, skilled, etc.) in the Introduction is attributable to accurate representation of
previous literature.
45
CHARACTERISTICS OF GOOD POKER PLAYERS
While it may well be the case that ability tends to increase with experience, there are
some situations where it does not. One study found that in a sample of experienced
players, those who were also classified as pathological gamblers had decision-making
skills that were comparable to those of inexperienced players (Linnet et al., 2012). It
should be pointed out that a potential gender confound exists in these studies. Most
notably, Linnet et al.’s (2010) experienced sample was composed solely of males,
whereas the inexperienced sample consisted solely of females. Palomäki et al.’s
(2013) study analyzed predominantly males, with females comprising only 16% of
their sample.
General Intelligence
Wechsler (1944) defined intelligence as the ‘‘ global capacity of the individual to act
purposefully, to think rationally and to deal effectively with his environment’’ (p. 3).
There has yet to be an empirical investigation of the relationship between poker skill
and general intelligence. Some poker bloggers, however, suggest that skill in poker
requires greater intelligence (e.g., see Badger, n.d.; Pinson, n.d.), wherein intelligence
is described as the ability to use multidimensional thinking and/or critical thinking.
Whether the suggested superior cognitive abilities asserted by poker bloggers can in
fact be empirically validated remains to be seen. Although it is not postulated herein
that superior intelligence is necessary for poker skill, it remains a possibility that
general intelligence does play a role in poker skill attainment.
Social Skills/Intelligence
Social intelligence has been shown to be a unique factor, distinct from general
intelligence (Ford & Tisak, 1983). Social intelligence is another area that seems likely to
play a role in poker playing skill. It certainly is the ability most endorsed in popular
culture as being necessary for successful poker playing. Social intelligence includes one’s
tendency to attend to social information and to process information observed in the
social environment, as well as being capable of controlling the social information
delivered via one’s own behaviour (Silvera, Martinussen, & Dahl, 2001). In the
literature, studies of poker players have provided support for the necessity of social
intelligence in successful poker playing. Bellin (2002), for example, notes that players
often introduce fake ‘‘ tells’’ in attempts to fool opponents (e.g., feigned excitement
suggesting a good hand, when the hand is actually weak). On a related note, one study
has shown that the best ‘‘ poker face’’ may be one that conveys trustworthiness rather
than neutrality (Schlicht, Shimojo, Camerer, Battaglia, & Nakayama, 2010). Schlicht et
al. (2010) showed that players more often folded to a bluffer who exhibited facial
characteristics associated with trustworthiness than to a bluffer who had a neutral facial
expression. Wilson (2003) states that both the ability to deceive and the ability to
recognize deception are crucial skills that aid in successful poker playing.
Opponent modeling is the act of perceiving and interpreting opponent behaviours
and adjusting one’s own strategy on the basis of this information (McCormack &
Griffiths, 2011). McCormack and Griffiths (2011), in a qualitative study, found that
46
CHARACTERISTICS OF GOOD POKER PLAYERS
the professional players (n= 4) created more accurate opponent models than did
recreational players (n= 5). Castaldo (2007), in an interview with a professional
female player, found that she would change playing strategies (e.g., choosing to bluff
more or less) depending on her perception of her opponents’attitudes towards her as
a female player. Slepian, Young, Rutchick, and Ambady (2013) found that
experienced players were able to accurately rate players’hand strength at above-
chance levels, based merely on arm movements used when the player was placing
bets (i.e., chips) into the centre of the table. Thus, abilities captured under the
umbrella of social intelligence, such as those used for opponent modeling, also
appear to contribute to the skill set of a successful poker player.
Personality and Risk Taking
Personality traits may also differentiate good and poor players. Palomäki et al.
(2013) found that greater self-evaluation, less rumination, and greater emotional
control occurred more frequently in their sample of experienced players. Greater self-
evaluation and less rumination is suggestive of lower levels of the personality trait
neuroticism. Greater emotional control lends itself to more logically sound decision
making. Browne (1989) also concluded, via an observational study, that better
players demonstrate greater emotional stability, as evidenced by their staying ‘‘ off
tilt.’’ To be ‘‘ on tilt’’ is to lose one’s temper and begin to make playing decisions on
the basis of emotion. Another observational study found that winners were more
gregarious than losers (Martinez & Lafranchi, 1969), and Brown and Mitchell (2010)
found that aggressive players were more likely to be extraverted.
Barrault and Varescon (2013) noted high sensation seeking among online poker
players. McCormack and Griffiths (2011) identified lower risk taking and more self-
discipline in professional players as compared with recreational players (i.e.,
professional players expressed that they were less likely to chase losses). Similarly,
Siler (2010) noted greater ‘‘ risk neutrality’’ associated with successful play. Risk
neutrality is evidenced when players consistently bet when the circumstances of the
hand/round (their own cards, perception of opponents’hand strength, etc.) suggest
positive expected value. It is possible that simply attending to expected value
facilitates greater risk taking (i.e., aggression), which is empirically associated with
more successful play. Therefore, the risk taking demonstrated by better players is
best seen as calculated rather than reckless.
Findings related to personality must be seen with healthy skepticism, as some of the
methodologies used are less than sound. For example, Brown and Mitchell (2010)
defined aggressive players simply as those individuals who played three or more of
10 hands observed. Other studies (e.g., Palomäki et al., 2013) provide results that
implicate personality differences but do not contain methodologies that objectively
assess personality. Rather, conclusions regarding individual differences in self-
control, for example, stem from qualitative judgements.
47
CHARACTERISTICS OF GOOD POKER PLAYERS
Other Differences
Three other studies have examined characteristics of poker players. One study
identified higher working memory capacity in their sample of better players (Meinz
et al., 2012). The other two studies reported a significant association between
gambling fallacies and problem gambling among poker players (MacKay, 2012;
Mitrovic & Brown, 2009).
Individual Differences Summary
Relatively little research has been reported on the characteristics differentiating good
poker players from poor poker players. Existing findings tentatively indicate that in
addition to better statistical knowledge about poker, the successful poker player
tends to have more playing experience, as well as higher social intelligence, greater
emotional control, and better working memory.
Study Goals
The goal of the current study is to begin to fill this void by attempting to more
comprehensively and rigorously examine the individual characteristics indicative of
skilled poker players. Specifically, the current study seeks to ascertain whether poker
skill is significantly related to (a) demographic characteristics (age, gender, race/
ethnicity, educational level); (b) educational achievement (i.e., grade point average
[GPA]); (c) general intelligence; (d) working memory; (e) general quantitative ability;
(f ) resistance to gambling fallacies; (g) social intelligence; (h) problem gambling
status; (i) risk perception and tolerance; and (j) personality. The findings of the
present study may also inform the debate regarding the legal standing of poker, the
debate regarding the taxation of poker players’profits, and the legitimacy of training
programs designed to improve poker skills. Finally, by identifying the individual
differences that are characteristic of skilled poker players—a novel undertaking in
and of itself—this research addresses a significant gap in the scientific literature.
Method
Participants
The final study sample consisted of 100 participants recruited from both University
of Lethbridge undergraduate students (82%) and Lethbridge community members
(18%). The sample, predominantly Caucasian (80%), consisted of 54 males and
46 females and had a mean age of 23.48 (SD = 6.45). To recruit the undergraduate
participants, we placed an advertisement on the university’s psychology participant
pool website, soliciting participants familiar with Texas Hold’em to participate in a
study investigating the factors that predict poker playing ability. Participants
recruited via this system received a 2% psychology course credit for their
participation, as indicated on their consent form. Community member participants,
whose consent forms did not discuss course credit, were recruited via word of mouth.
48
CHARACTERISTICS OF GOOD POKER PLAYERS
Word-of-mouth recruitment was used to ensure a broader demographic sample (e.g.,
age, playing experience, education level), as well as a greater variation of skill level
within the sample. The consent form for both sets of participants indicated that,
depending on their demonstrated poker skill, they would receive between $0 and
$100 in the form of a Visa gift card. This was done to increase motivation and
provide ecological validity for the task. This study was reviewed and approved by the
University of Lethbridge Human Subject Research Committee.
Materials
Each participant engaged in a total of 12 tasks, including a detailed collection of
demographic information, a virtual Poker Playing Assessment, our experimental
Poker Skills Measure, and a series of individual difference measures. Table 1
itemizes all experimental tasks undertaken, task order by condition (an attempt was
made to counter-balance the presentation of some tasks), and approximate time to
complete each task. All experimental tasks and measures are described in the
following subsections.
Demographics and Poker Playing Survey. The Demographics and Poker
Playing Survey was designed specifically for this study. Information was collected
on age, sex, ethnicity, years of education, and university major. For students, their
GPA was also collected (maximum = 4.0). In addition, we collected years of poker
Table 1
Experimental Tasks, Task Times, and Task Order by Condition
Task
Time
(min)
Condition 1
(n= 24)
Condition 2
(n= 24)
Condition 3
(n= 27)
Condition 4
(n=25)
Consent 3 S1-1 S1-1 S1-1 S1-1
Demographics 3 S1-2 S1-9 S2-1 S2-8
Stanford-Binet Matrices*15 S1-3 S1-8 S2-2 S2-7
Digit span 3 S1-4 S1-7 S2-3 S2-6
Stanford-Binet Equation Building*15 S1-5 S1-6 S2-4 S2-5
Poker Quantitative
a
5 S1-6 S1-5 S2-5 S2-4
Gambling Fallacies Measure 5 S1-7 S1-4 S2-6 S2-3
Tromso Social Intelligence Scale 5 S1-8 S1-3 S2-7 S2-2
PSM1 22 S1-9 S1-2 S2-8 S2-1
PSM2
b
S2-1 S2-5 S1-2 S1-6
PPGM
c
4 S2-2 S2-4 S1-3 S2-5
DOSPERT
d
7 S2-3 S2-3 S1-4 S2-4
NEO-Personality Inventory 25 S2-4 S2-2 S1-5 S2-3
Poker Playing Assessment 15 S2-5 S2-1 S1-6 S2-2
Note. Time = approximate time, in minutes, to complete each experimental task. Conditions 1 through 4 present task orders;
n= number of participants included in final sample by condition. Condition task orders are represented by S = Session
number (1 or 2) and task number (e.g., S2-3 = Session 2, 3rd task completed). Tasks marked with an asterisk (*) were timed
tasks; thus the time to complete is the maximum allotted time, rather than an approximate time.
a
This 10-item paper-and-pencil test of poker quantitative skill is not used or mentioned in the subsequent analyses, as it was
thought to be too closely related to the Poker Skills Measure (PSM).
b
PSM2 was administered for test-retest purposes and was
included for only 50 participants.
c
Problem and Pathological Gambling Measure.
d
Domain-Specific Risk-Taking Scale.
49
CHARACTERISTICS OF GOOD POKER PLAYERS
playing experience, typical poker playing habits (i.e., online vs. live play; with
friends/family vs. strangers), and self-rating of playing ability (assessed by making a
vertical mark along a horizontal line with anchoring endpoints described as novice
and expert and converting the mark to a score from 0 to 100).
General intelligence. Participants completed the Matrices subtest of the
Stanford-Binet Intelligence Scale, 4
th
edition (Thorndike, Hagen, & Sattler, 1986).
The 26 items in this subscale from the Abstract/Visual Reasoning Area of the
Stanford-Binet provide a pictorial matrix of either four or nine items with one cell
blank. The person uses his or her reasoning ability to determine the pattern or
principle contained in the matrix so as to determine which of the four options
provided best fits the missing cell. The Matrices subtest is normally untimed, but
participants in the present study were given 15 min to complete it. The Matrices
subtest is modeled after the Raven Progressive Matrices, which is intended to be a
culture-free measure of general intelligence (g). Factor analytic studies have
confirmed that the Stanford-Binet Matrices is a good measure of g(accounting for
55% of the variance) and has a Pearson correlation of.78 with the overall Stanford-
Binet Composite IQ (Sattler, 1988).
Digit span task. This digit span task (Della Sala, Foley, Beschin, Allerhand, &
Logie, 2010) is intended to be a measure of working memory capacity. For this task,
the experimenter reads a list of numbers with a 1-s delay between each number.
Participants are then required to repeat the list back. Six lists per digit span length
are used, and testing ends when a participant repeats fewer than five of the six lists
correctly. Participants’scores are recorded as the greatest span that the person was
able to accurately reproduce.
Quantitative ability. The 18-item Equation Building subtest is from the
Quantitative Reasoning Area of the Stanford-Binet Intelligence Scale, 4
th
edition
(Thorndike et al., 1986). This subtest requires respondents to use given numbers and
numerical operators to create a mathematical equation. For example, given the
following information: ‘‘ 235=+,’’ respondents would create the true mathematical
statement: ‘‘ 2+3=5.’’ Although this test is normally untimed, participants were
again given 15 min to complete it. This measure assesses respondents’working
understanding of numerical operations and is intended to be a measure of
crystallized quantitative ability (Sattler, 1988). The Equation Building subtest has
a test-retest reliability of .91 (Sattler, 1988).
Gambling Fallacies Measure. The Gambling Fallacies Measure (Williams,
2003) is a 10-item questionnaire developed to assess erroneous beliefs associated with
gambling. By assessing respondents’ability to take statistical probabilities and the
random nature of most gambling games into account, this measure assesses
respondents’tendency to succumb to (or to resist) gambling fallacies, including
the illusion of control, the perception of personal luck, and the gambler’s fallacy.
Internal reliability is low (Cronbach’s alpha = .51), which reflects the fact these
10 questions assess a wide range of different fallacies. However, the 1-month test-retest
50
CHARACTERISTICS OF GOOD POKER PLAYERS
reliability of this measure is relatively good (r= .70). Its validity is established by its
significant correlation with problem gambling status, gambling frequency, number of
gambling activities engaged in, and paranormal beliefs (Williams, 2003).
Tromso Social Intelligence Scale (TSIS). The TSIS measures three components
of social intelligence: Social Information Processing (SP), Social Skills (SK), and
Social Awareness (SA; Silvera et al., 2001). Silvera et al. (2001, Study 3) report the
internal reliabilities for each subscale (SP, SK, and SA) to be a= .79, .85, and .72,
respectively. The criterion and construct validity of this scale has also been
established (Silvera et al., 2001, Study 1; Tayfun & Cetin, 2009). The TSIS consists of
21 items and yields three scores for each of the three components of social
intelligence. A social intelligence composite score is also derived.
Poker Skills Measure (PSM). The PSM (Leonard, Staples, & Williams, 2014)
measures respondents’current poker skill level. For each of the 35 items in this measure,
respondents are presented with a poker scenario for which they must decide which playing
action is most appropriate. The scenarios presented tend to increase in complexity from
Scenario 1 to Scenario 35. The scenarios vary in terms of which stage of the game is
occurring (pre-flop, flop, turn, river), the documented actions or inactions of the other
players at the table, the number of other players remaining in the hand (two to six), the
amount that has been bet, and the described playing style of the opponents (tight/loose;
aggressive/passive). Each question is presented on a single page with a colour pictorial and
text. Participants are also provided with a glossary of Texas Hold’em terminology, as well
as a tutorial page that itemizes each pictorial component (e.g., folded cards, cards in play,
pot and stack sizes). Respondents are provided with three response actions for each
scenario. The PSM has a test re-test reliability of .82 and high internal consistency
(Cronbach’sa= .79); its validity is established by its significant correlation with objective
measures of poker playing performance.
Problem and Pathological Gambling Measure (PPGM). The PPGM measures
respondents’self-reported gambling behaviour over the past 12 months. This instrument
contains questions pertaining to all areas of potential harm related to gambling and has
been shown to be better able to detect problem gamblers who are in denial than are
other commonly used measures (Williams & Volberg, 2010, 2014). The PPGM yields
high classification accuracy (sensitivity = 94.44%, specificity = 99.81%), minimizing both
false positives and false negatives, which is confirmed by high agreement (k=.93)
between the instrument and clinical assessment (Williams & Volberg, 2010, 2014).
Domain-Specific Risk-Taking (Adult) Scale (DOSPERT), Risk Perception subscale.
The DOSPERT Scale Part II (Blais & Weber, 2006) is a 30-item scale that assesses risk
tolerance for decisions in five domains: financial (separate subscales for investing vs.
gambling), health/safety, recreational, ethical, and social. The reported internal
consistency for these domains is adequate at .74, .83, .74, .79, and .83, respectively
(Blais & Weber, 2006). The DOSPERT Part II Scale used herein is the short version of
the original DOSPERT for which convergent and discriminant validity were established
51
CHARACTERISTICS OF GOOD POKER PLAYERS
and for which internal consistency values similar to those reported for the long version
were obtained (Weber, Blais, & Betz, 2002).
NEO Personality Inventory Revised Edition (NEO-PI-R). The NEO-PI-R
provides a measure of the five personality domains, Neuroticism, Extraversion,
Openness, Agreeableness, and Conscientiousness, as well as six subfacets associated
with each of the five domains (Costa & McCrae, 1992a). Thus, there are 30 facets in
all. Scores for each domain are the summation of relevant facet scores. The NEO-PI-
R is currently the dominant instrument in the assessment of personality. Its validity,
concurrent and discriminant, has been well established in both normal and clinical
populations (Costa & McCrae, 1992a). Internal reliability of the domain scores are
high, ranging from .86 to .92, and the internal reliabilities of the facets range from .58
to .82 (Costa & McCrae, 1992b).
Poker Playing Assessment.
2
For this assessment, participants were asked to
play 30 hands in a virtual game of no limit Texas Hold’em against artificial
intelligence (AI) players (maximum of five AI players) on a laptop computer. No-Limit
Hold’em Cash Game Version 1 (2011) was used, as this program (a) uses a random
number generator to determine cards dealt; (b) allows for automatic buy-ins (e.g., if
a player loses all of his or her money, the account is automatically replenished so that
the person can continue playing); and (c) allows for the manipulation of both the skill
level and the style of play of the AI players. It was statistically determined that playing
30 hands reduced the overall pre-flop equity variance between players to 4%. With
a maximum of 4% pre-flop equity variance, it was thought that no participant would
be unduly advantaged or disadvantaged by playing so few hands. This program did
not permit the predetermined selection of cards dealt so as to altogether eliminate
variability in the pre-flop equity between players. Also unclear is the extent to which
30 hands were sufficient to (a) eliminate the variability in the strength of the post-flop
cards between participants and (b) eliminate the variability in the strength of the
opponents’hands between participants. We suspect that many more than 30 hands
would have been needed.
The five AI opponents had a wide range of skill levels and playing styles to simulate
what often happens in social games of poker. More specifically, there were two loose/
aggressive players, two tight/aggressive players, and one loose/weak player. ‘‘ Loose’’
players are defined as people who play more hands and tend to continue with weaker
hands; hence, they do not often fold. ‘‘ Tight’’ players play fewer hands and tend not to
continue with weaker hands; hence, they often fold. An ‘‘ aggressive’’ player is more
likely to bet and raise compared with a ‘‘ passive’’ player, who is more likely to check
and call. ‘‘ Loose/weak’’ players differ from loose/passive players in that (a) they often
will not fold prior to all community cards being dealt and (b) they tend not to adjust
their playing style (e.g., play more aggressively when holding a good hand).
2
Because this assessment was used to evaluate unrelated working hypotheses, the results associated
with it are not discussed herein.
52
CHARACTERISTICS OF GOOD POKER PLAYERS
After 30 hands, participants’net profit, number of hands folded pre-flop, percentage of
hands won, percentage of hands raised pre-flop, and percentage of hands bet on the flop
were recorded. For the purpose of participant remuneration, a composite score for the
Poker Playing Assessment was derived by averaging the rank earned on four variables: net
profit, hands won, aggression (bets) at pre-flop, aggression (bets) at the flop. Betting
aggression is generally correlated with skill level because increasing the price to stay in a
round (a) has a tendency to induce players with stronger cards to fold and/or (b) increases
the payoff from players who remain in the round with weaker cards (Potter van Loon, van
den Assem, & van Dolder, 2015; Siler, 2010). In general, skilled poker players have also
been documented to play fewer hands (‘‘ playing tight‘‘ ) than poorer players, reflective of
their better understanding that only a minority of hands have a good chance of winning
(Siler, 2010). So as not to penalize better players for this tendency, the percentage of hands
won was calculated as follows: (total number of hands won divided by total number of
hands played) multiplied by 100. Participant remuneration was based on this composite
ranking averaged with participant ranking on the PSM.
Procedure
Each participant was tested individually, in two 1-hr sessions, spaced 1 week apart.
Written informed consent was obtained prior to the commencement of Session 1. Two
word-of-mouth participants were not of the age of majority at the time of data collection.
Written informed consent was given by these participants’parents and written informed
assent given by the participants. Verbal indication of continued consent was sought before
engaging in Session 2. As part of written informed consent, in addition to the 2% course
credit indicated for undergraduate participants, all participants were informed that they
could potentially be eligible for financial remuneration. It was explained that their
eligibility for receiving monetary compensation would be based on their ranking,
compared with the other participants, on a composite score of both poker playing
measures (the PSM and the Poker Playing Assessment). They were reminded of this fact
when we introduced the PSM and the Poker Playing Assessment.
After informed consent was obtained, participants were assigned to one of four test
order conditions. Four different experimental task orders were used so as to reduce
order effects such as fatigue while also preventing potential priming effects (if, for
example, the PSM and the Poker Playing Assessment were completed in succession).
Instructions preceded each experimental task and were delivered either verbally by
the experimenter, or they were included in the written instructions provided with the
task. The consent form and all measurements were completed in a paper-and-pencil
format with the exception of the Poker Playing Assessment, which was conducted on
a 17-inch Acer laptop computer in full-screen mode.
Upon completion of the second session, participants were thanked and debriefed,
and they were told that remuneration would follow after all data were collected.
The top poker performer received $100, those ranked 2 to 10 received $50 each, those
ranked 11 to 40 received $30 each, those ranked 41 to 75 received $25 each, and
those ranked below 75 received $0.
53
CHARACTERISTICS OF GOOD POKER PLAYERS
Results
Data Screening and Cleaning
Less than 0.005% of data were missing. Of these, most missing data points were from
the NEO personality questionnaire. Missing values from the NEO were replaced
with the individual’s mean score for the personality facet from which the data point
was missing. Two participants each left one answer blank on the Gambling Fallacies
Measure. Scores for these participants were calculated out of nine rather than 10.
Twelve percent of data was missing from the GPA scores, due either to participants
not being students or to participants not knowing or reporting their GPA. This
variable was omitted from the multivariate analysis because of the large proportion
of missing data.
For regression analysis, all variables were assessed for skew above or below 0.4 and
for outliers, with outliers defined as having a standard score of ±3.29. Outliers were
detected in age, total playing experience, the PPGM composite, the NEO domains
Neuroticism and Agreeableness, the NEO facets Activity level (Extraversion
domain) and Tendermindedness (Agreeableness domain), and the Social skills
subsection of the TSIS. Outliers accounted for less than 0.9% of all data, and all
outliers were determined to be accurate data points because participants whose age
and playing experience contributed to the detection of outliers in these variables were
known to the experimenter. Outliers in all other variables were considered accurate
because, despite being statistically rare, they were not errors in data entry or
impossible figures (e.g., scores beyond the response endpoints on a given survey).
Thus, original values were retained (and reported) for the descriptive statistics. An
inverse transformation corrected for outliers and non-normality of the PPGM
composite variable. As no transformation adequately corrected for skew and outliers
in these variables, the variables were winsorized. Winsorization significantly reduced
the skew of Agreeableness, Activity level, and Social Skills. Winsorization removed
the outliers and attenuated the skew of Neuroticism, Tendermindedness, age, and
total playing experience). Of final note, a point biserial correlation was conducted for
the dichotomous variables of gender (male; female) and ethnicity (Caucasian; non-
Caucasian).
Univariate Results
Pearson’srcorrelations were calculated between PSM scores and all the individual
difference measures. As can be seen in Table 2, surprisingly few variables were
significantly associated with PSM scores, and the magnitude of the correlation was
low for the few that were.
The relationship between poker skill and all five NEO personality domains
(Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness) was
near zero. Similarly, there was no significant correlation with general intelligence
54
CHARACTERISTICS OF GOOD POKER PLAYERS
Table 2
Full Sample Assessment Scores and Correlation with Poker Skills Measure
Assessment
Scores (n= 100)
MSDMin Min Pearson’srFDR
Gender .54 0 1 .38** .00
Ethnicity .80 0 1 .007
Age 23.28 6.45 17 57 .17
Grade point average 3.14 0.55 1.50 4.00 -.01
Years of education 14.58 2.07 10.64 20 -.04
Poker experience 4.68 5.13 0 40 .26** .05
Digit span 5.6 1.02 4 8 .16
SB Matrices 55.19 7.29 41 68 .13
SB Equation Building 57.65 10.45 35 76 .04
Gambling Fallacies Measure 7.36 1.52 3 10 .26** .03
Risk Perception
Ethical 30.00 4.96 15 40 .06
Monetary 29.34 5.11 16 40 -.25*.03
Gambling only 17.05 3.41 7 21 -.21*.05
Investing only 12.29 3.09 6 21 -.19
Health/Safety 27.11 6.09 11 39 -.05
Recreational 23.14 6.76 8 38 .09
Social 16.42 4.92 7 26 -.001
Social IQ
Social Information Processing 5.21 0.69 3.29 7 .27** .03
Social Skills 4.81 .07 2.43 6.14 .12
Social Awareness 5.12 .82 3 7 .10
Social IQ composite 105.94 12.41 78 137 .20*.05
PPGM total score
a
.045 1.06 0 5 -.24*.04
Personality domains and facets
Extraversion 3.56 0.44 2.58 4.58 -.05
Assertiveness 3.25 0.64 1.75 4.75 -.006
Activity level 3.27 0.56 1.38 4.50 -.02
Excitement seeking 3.81 0.54 2.5 5.00 -.05
Gregariousness 3.36 0.72 1.62 5.00 .03
Positive emotions 3.79 0.67 1.88 5.00 -.06
Warmth 3.88 0.57 2.62 5.00 -.06
Agreeableness 3.37 0.45 1.58 4.29 -.05
Trust 3.27 0.71 1.38 5.00 .06
Straightforwardness 3.21 0.59 1.62 4.50 -.15
Altruism 4.00 0.56 2.25 5.00 -.09
Meekness 2.96 0.67 1.12 4.50 .08
Modesty 3.23 0.73 1.00 4.88 -.15
Tendermindedness 3.56 0.56 1.62 4.75 .06
Conscientiousness 3.48 0.41 2.54 4.60 -.06
Competence 3.55 0.42 2.62 4.5 .10
Achievement 3.50 0.55 2.12 4.75 -.12
Discipline 3.53 0.71 2.00 5.00 -.08
Order 3.36 0.56 2.12 4.50 -.07
Dutifulness 3.74 0.51 2.62 5.00 -.05
Deliberation 3.21 0.68 1.62 4.50 -.01
55
CHARACTERISTICS OF GOOD POKER PLAYERS
(Stanford-Binet Matrices), working memory capacity (digit span), quantitative skills
(Stanford-Binet Equation Building), GPA, age, or years of education.
However, PSM had a significant negative relationship with two personality facets
from the Openness domain: Aesthetics (r= -.20, p= .04) and Fantasy (r= -.23,
p= .02). This result is a reflection that appreciation of art and beauty, as well as
having a rich fantasy life and imagination, are both negatively related to poker skill.
Poker skill was also significantly related to lower levels of gambling fallacies (r= .26, po
.01; higher scores on the GFM indicate greater resistance to gambling fallacies). This result
implies that the behaviour of good poker players is more strongly guided by the statistical
probabilities involved rather than by hunches, beliefs, and other erroneous notions.
On the DOSPERT, poker skill was also negatively related to a perception that
engaging in gambling (r= -.21, p= .04) or financial investment (r= -.19, p= .06)
constituted a significant risk. The overall correlation with the Monetary scale was
also significant (r= -.25, po.01).
A significant positive relationship was obtained between poker skill and the
Information Processing section of the TSIS (r= .27, po.01), indicating higher levels
of social information processing among skilled players.
Of final note, a significant relationship was established between poker skill and an
inverse transformation of the PPGM composite score (r= -.24, p= .02). This result
Table 2. Continued
Assessment
Scores (n= 100)
MSDMin Min Pearson’srFDR
Neuroticism 2.79 0.45 1.29 3.65 -.02
Anxiety 3.01 0.66 1.12 4.50 -.07
Hostility 2.71 0.63 1.25 4.62 -.08
Depression 2.68 0.69 1.12 4.25 .02
Self-consciousness 2.77 0.67 1.00 4.38 .05
Impulsivity 3.33 0.56 2.00 4.50 .05
Vulnerability 2.25 0.52 1.00 3.62 -.07
Openness 3.54 0.43 2.56 4.54 -.11
Aesthetics 3.22 0.82 1.38 5.00 -.20*.05
Fantasy 3.44 0.70 1.50 5.00 -.23*.04
Feelings 3.64 0.64 2.25 5.00 -.07
Ideas 3.72 0.68 2.00 5.00 .11
Actions 3.31 0.42 2.38 4.25 -.09
Values 3.88 0.53 2.25 5.00 .09
Note. Min = lowest score detected; Max = highest score in sample; FDR = false discovery rate adjusted probability; SB =
Stanford-Binet; PPGM = Problem and Pathological Gambling Measure.
a
Correlation calculated with inverse transformation of PPGM scores.
*pp.05. **pp.01.
56
CHARACTERISTICS OF GOOD POKER PLAYERS
indicates that problem gambling symptomatology is greater among more skilled
poker players.
Multivariate Results
Multiple univariate correlations capitalize on the chance occurrence of significance.
In addition, they do not indicate the unique contribution of each variable to poker
skill. Thus, a multiple regression was also undertaken. Univariate outliers and skew
were corrected as previously explained. A number of variables were excluded so as to
eliminate singularity and/or multicollinearity, as well as to reduce the overall number
of independent variables in light of the relatively small sample size of 100. More
specifically, the following variables were not included in the multiple regression:
social intelligence composite score, all subfacets of each of the personality domains,
the Extraversion domain, and the two subareas of the Monetary section of the
DOSPERT. GPA was also excluded because 12% of the data was missing. All other
variables were included. No multivariate outliers were found as assessed by
Mahalanobis distance (w
2
X49.7). All other variables were entered simultaneously.
The poker skill index (PSM score) was significantly related to the combination of
individual difference measures, F(22, 77) = 2.60, p= .001. The adjusted R-squared
value was .26, indicating that in this sample, 26% of the variance in the poker skill
scores can be accounted for by the combination of these individual differences.
Table 3 displays the unstandardized and standardized regression coefficients, as well
as the semi-partial squared correlations. Variables are listed in order of largest to
lowest standardized regression coefficient. Only two variables contributed signifi-
cantly to the prediction of higher PSM scores: Social Information Processing and
gender. However, four additional variables approached significance: scores on the
Gambling Fallacies Measure (p= .07), age (p= .09), and both the Financial subscale
(p= .09) and the Ethical subscale (p= .07) of the DOSPERT.
Highest Skill Cases
The multiple regression analysis identified several variables that are generally
associated with skill levels, but these variables do not indicate whether having
strength in all of these attributes is required for individual poker success, or whether
having strength in any one or more would be sufficient. Thus, a final analysis
examined the consistency of individual attributes among the highest skilled players in
the sample: those with PSM scores greater than 2 SDs above the mean PSM score.
Four participants fit the criteria for this analysis of having PSM scores greater than
2 SDs above the mean; their scores were 32, 32, 29, and 29.
The scores of each case participant, as well as the mean scores of the whole sample,
can be seen in Table 4. Variables where all four players scored above or all scored
below the average are in bold.
This table illustrates that, as expected, the four skilled players all had higher than
average self-rated poker ability, played fewer hands, and had higher flop aggression.
57
CHARACTERISTICS OF GOOD POKER PLAYERS
The other variables where all four players were consistently different from average
were male gender, higher intelligence, greater resistance to gambling fallacies, lower
perception of risk involved in gambling and investing, better social information
processing, better social skills, lower modesty, lower openness, and lower aesthetic
appreciation. The fact that the highest skilled players consistently scored higher or
consistently scored lower than most people on these variables contributed to these
variables being identified as statistically important in both the univariate and
multivariate analyses. Despite this obvious confound, however, (a) the consistency in
these attributes across the four players, (b) the fact that virtually all of these
attributes were previously identified in either the multivariate and/or univariate
analysis, and (c) the low magnitude of these univariate and multivariate correlations
suggests that having sufficient levels of most of these attributes is more important for
poker success than having exceptional strength in just one or two of these areas.
Discussion
The current study was conducted to comprehensively examine the individual
characteristics indicative of good poker players. First, we found that age, race/
Table 3
Multiple Regression Results
Regression
Coefficients (B)
Standardized Regression
Coefficients (b)
Squared Semi- Partial
Correlations (sr
i2
)
Social Information Processing 2.21 0.27*0.03
Gender 2.74 0.25*0.04
Age 0.25 0.21 0.02
Risk - Ethical 0.24 0.21 0.03
Gambling Fallacies Measure 0.68 0.18 0.02
Risk - Financial -0.19 -0.18 0.02
Risk - Health/Safety -0.15 -0.17 0.01
Years of education -0.41 -0.15 0.02
Neuroticism 1.67 0.14 o0.01
Openness -1.75 -0.13 0.01
PPGM -7.06 -0.13 0.01
Conscientiousness -1.43 -0.11 o0.01
Stanford-Binet Matrices 0.07 0.09 o0.01
Playing experience (years) 0.11 0.07 o0.01
Digit span 0.38 0.07 o0.01
Risk - Recreational 0.05 0.07 o0.01
Agreeableness -0.62 -0.05 o0.01
Social Awareness 0.29 0.04 o0.01
Ethnicity -0.54 -0.04 o0.01
Stanford-Binet Equation Building 0.02 0.03 o0.01
Social Skills 0.18 0.02 o0.01
Risk - Social 0.02 0.01 o0.01
Constant -0.39
Note. PPGM = Problem and Pathological Gambling Measure.
*pp0.05.
58
CHARACTERISTICS OF GOOD POKER PLAYERS
Table 4
Highest Skill Participant Scores Versus Total Sample Scores
Sample Scores (n= 100) Case Scores
Assessment MSDCase 1 Case 2 Case 3 Case 4
PSM 17.63 5.57 29 29 32 32
Age 23.28 6.45 23 23 20 22
Gender
a
MMMM
Ethnicity
b
CCCC
Self-rated poker ability (%) 33.42 21.15 68 87 65 67
Playing experience (years) 4.68 5.13 2.5 10 7 3
Digit span 5.6 1.02 4 6 7 7
Stanford-Binet Equation Building 57.65 10.45 70 46 56 72
Stanford-Binet Matrices 55.19 7.29 60 64 60 60
Gambling Fallacies Measure 7.36 1.52 88910
Risk Perception
Ethical 30.00 4.96 28 31 30 33
Financial 29.34 5.11 22 19 26 20
Gambling only 17.05 3.41 10 10 16 13
Investment only 12.29 3.09 12 9 10 7
Health/Safety 27.11 6.09 25 27 31 16
Recreational 23.14 6.76 26 35 21 17
Social 16.42 4.92 18 9 15 23
Social IQ
Social Information Processing 5.21 0.69 5.43 7.00 6.57 5.71
Social skills 4.81 .07 4.86 6.14 5.86 5.29
Social Awareness 5.12 .82 3.71 6.43 5.86 5.29
Composite Social IQ 15.14 1.78 13.90 19.57 18.29 16.29
Composite PPGM .045 1.06 2 3 0 3
Personality domains and facets
Extraversion 3.56 0.44 3.21 3.56 3.71 3.88
Assertiveness 3.25 0.64 3.25 3.13 4.38 3.13
Activity level 3.27 0.56 2.13 3.25 4.25 3.5
Excitement seeking 3.81 0.54 3.88 2.88 3.63 4.38
Gregariousness 3.36 0.72 3.00 3.63 3.38 3.89
Positive emotions 3.79 0.67 3.38 4.00 3.25 4.63
Warmth 3.88 0.57 3.63 4.50 3.38 3.75
Agreeableness 3.37 0.45 2.50 3.92 2.83 3.63
Trust 3.27 0.71 2.50 4.75 3.25 3.38
Straightforwardness 3.21 0.59 2.00 3.63 2.38 3.13
Altruism 4.00 0.56 2.50 4.88 3.00 4.50
Meekness 2.96 0.67 2.13 3.38 2.38 3.50
Modesty 3.23 0.73 2.50 3.13 2.13 2.88
Tendermindedness 3.56 0.56 3.38 3.75 3.88 3.38
Conscientiousness 3.48 0.41 2.58 3.71 4.21 2.88
Competence 3.55 0.42 3.25 3.63 4.38 3.63
Achievement 3.50 0.55 2.63 3.75 4.25 2.75
Discipline 3.53 0.71 2.00 3.75 4.88 3.00
Order 3.36 0.56 2.38 3.50 4.25 2.63
Dutifulness 3.74 0.51 2.75 4.13 4.25 3.13
Deliberation 3.21 0.68 2.50 3.50 3.25 2.13
59
CHARACTERISTICS OF GOOD POKER PLAYERS
ethnicity, educational level, and educational achievement were not significantly
correlated with poker playing skill. None of these variables had a strong theoretical
basis for expecting them to be associated with poker skill, and so the failure to find a
relationship was not unexpected.
More surprising is the fact that being male was significantly and consistently related
to poker skill. Although most professional poker players are male, we presume that
cultural attributes associated with male gender (e.g., aggression), rather than
biological attributes of male sex, facilitated success. Being female certainly does not
preclude one from being an excellent player, as there are several well-known
professional female poker players. Nonetheless, as poker remains a male-dominated
game, it is also possible that females—despite knowing how to play, and even
playing well—tend to devote less time or attention to the game to hone their skill and
perhaps have a less competitive drive to win at the game.
We also found general intelligence to have little or no relationship to poker skill level
or poker skill attainment. Higher intelligence was never postulated as a necessary
attribute (and certainly does not appear to be a preeminent feature among the
world’s best poker players). Thus, it is not surprising that high intelligence was not
strongly related to poker skill, although it is still possible that at least average or
above average levels are required.
More surprising is the failure to find a relationship between quantitative ability and
poker skill. As poker is recognized to be a game in which mathematical ability is
Table 4. Continued
Sample Scores (n= 100) Case Scores
Assessment MSDCase 1 Case 2 Case 3 Case 4
Neuroticism 2.79 0.45 2.89 2.65 2.60 2.60
Anxiety 3.01 0.66 3.00 3.13 2.63 2.25
Hostility 2.71 0.63 3.00 1.63 3.13 2.13
Depression 2.68 0.69 2.50 2.75 2.25 2.00
Self-consciousness 2.77 0.67 3.25 2.38 2.75 2.63
Impulsivity 3.33 0.56 3.50 4.00 3.25 4.38
Vulnerability 2.25 0.52 2.13 2.00 1.63 2.25
Openness 3.54 0.43 3.52 3.48 3.15 3.48
Aesthetics 3.22 0.82 3.13 2.63 2.13 2.88
Fantasy 3.44 0.70 3.50 3.75 2.13 3.50
Feelings 3.64 0.64 3.50 4.38 3.50 2.75
Ideas 3.72 0.68 4.63 2.00 4.13 4.25
Actions 3.31 0.42 3.13 3.75 2.88 3.38
Values 3.88 0.53 3.25 4.38 4.13 4.13
Note. PSM = Poker Skills Measure; PPGM = Problem and Pathological Gambling Measure. Total sample scores were
compared with case scores, which are the scores of the four participants who scored at least 2 SDs above the group mean on
the PSM. Variables where all four players scored above or all scored below the average are in bold.
a
M = Male. Total sample included 54 males and 46 females.
b
C = Caucasian. Total sample included 80 Caucasians.
60
CHARACTERISTICS OF GOOD POKER PLAYERS
necessary, this finding may seem counterintuitive. There are two possible
explanations for this finding. First, when one reviews the types of calculations
required in the game of poker, it becomes evident that much of the math is relatively
simple. In determining the likelihood of winning, for the most part, a poker player
(a) simply adds up the number of remaining cards in the undealt deck that could
complete the hand being created, (b) judges the likelihood that these cards will
appear in the five community cards, and (c) is familiar with the strength of that
particular hand if it does appear. Thus, the quantitative abilities necessary for poker
skill may not demand a high level of mathematical ability. Second, poker skill was
consistently associated with low levels of gambling fallacies. Many gambling fallacies
hinge on the misunderstanding of statistics. The fact that better poker players are
more resistant to fallacies suggests that they understand the basic tenets of statistics.
Understanding statistics, however, is both a specific and a learned ability. Thus, it is
possible that better poker players have attained a greater understanding of the
specific mathematical calculations and statistics necessary for successful poker
playing without necessarily increasing their general quantitative skills. Put another
way, although exceptional quantitative skills may not be necessary to poker success,
basic quantitative ability and adherence to poker-specific statistical probabilities is
essential.
One component of social intelligence was consistently found to be significantly
related to PSM scores: social information processing. High scores on this component
of social intelligence speak to an individual’s ability to accurately interpret the
behaviour of others. This finding supports previous research that indicates that better
players make mental models of opponents (Castaldo, 2007; McCormack & Griffiths,
2011; Wilson, 2003) and, at least in part, use this information to direct their own
playing strategy. That the relationship detected between PSM scores and this
component of social intelligence was only moderate, and that no other component of
social intelligence was found to significantly relate to poker skill, indicates, however,
that high social intelligence is neither essential nor sufficient in the making of a
skilled poker player.
Working memory capacity was a significant predictor of poker performance in one
prior study (Meinz et al., 2012). The current study findings did not indicate any
relationship between working memory, evaluated by a digit span task, and poker
skill, nor was there a consistent trend, higher or lower, in the working memory
capacity of the four high PSM scorers in comparison to the whole sample. Thus, it
does not appear that working memory capacity has any important bearing on poker
skill level or poker skill attainment, although it is always possible that working
memory evaluated by different means would reveal results similar to those of
previous research (e.g., Meinz et al., 2012, used one verbal and one spatial complex
span task).
Previous research regarding risk perception and tolerance found that better poker
players have a greater tolerance for poker-specific risk taking (i.e., betting/raising; Siler,
2010). In the current study, risk perception was assessed across five domains: health/
61
CHARACTERISTICS OF GOOD POKER PLAYERS
safety, recreational, social, ethical, and financial. Findings from the current study add
support for, and extend, previous findings. Better poker players demonstrated greater
tolerance for all financial risk, rather than only for gambling-specificfinancial risk.
Importantly, no other significant relationships were detected between poker skill and
risk perception or tolerance. This finding indicates that despite the greater tolerance for
financial risk, better poker players are not more tolerant of risk in general.
Previous studies indicated that better players had traits indicative of extraversion
such as gregariousness (Martinez & Lafranchi, 1969) and sensation seeking (Barrault &
Varescon, 2013) and that better players were lower in traits presumably associated with
neuroticism such as rumination (Palomäki et al., 2013). In the current study, however,
no relationships between PSM scores and personality domains or facets were found that
would support previous research claims. In fact, with two exceptions, there was
virtually no association between any aspect of personality and poker skill. The two
exceptions to this finding were the significant negative relationships detected between
poker skill and two personality facets of the Openness domain: Aesthetics and Fantasy.
Low scores for the Fantasy personality facet are indicative of individuals who prefer
practicality and realism. Individuals who score low on the personality facet Aesthetics
are not swayed by art and beauty. It could be that the tendency of better players
towards realism, both in the avoidance of fantasy and of art and beauty, aids their
poker playing by reducing susceptibility to distraction (e.g., maintain focus on the game
rather than slipping off into a spell of daydreaming).
The final individual characteristic of interest in the current study was susceptibility to
problem gambling behaviours. Susceptibility to problematic gambling behaviour did
increase significantly in association with higher skill. Specifically, higher skilled players
reported a greater tendency to spend more time and money on gambling than planned,
as well as problems with family or spouse because of the time spent gambling. These
findings suggest that better players tend towards over-involvement with the game of
poker. It may well be the case that the time spent playing poker, however, at least in
part, is a facilitator of poker skill attainment. This possibility is supported by the
significant univariate correlation between years of playing poker and poker skill.
Afinal observation concerns the fact that the four most skilled players had similar
profiles, with above average (but not exceptionally high) levels of virtually all of the
above statistically important variables. This, combined with the observation that the
magnitude of all of the statistically significant variables was low, suggests that the
profile of a successful poker player is someone who has the requisite levels of all of
these attributes, rather than exceptional strength in just one or two of these areas.
Implications
The current study findings lead to two important practical implications. First, these
findings contribute to the ongoing legal skill versus chance debate. Herein it was
found that individual characteristics are significantly associated with, or aid in, poker
playing skill. Success in poker, therefore, rather than being solely reliant on chance,
62
CHARACTERISTICS OF GOOD POKER PLAYERS
is influenced by individual characteristics (e.g., social information processing) that
contribute to a player’s ability to influence game outcomes.
No specific individual difference was found to be sufficient for greater poker skill
level, as having a profile of statistically important individual differences (i.e., greater
financial risk tolerance, greater social information processing skills, etc.) appears to
provide the foundation on which poker players can build skill. To an extent,
individual differences that were found to relate to skill level were differences that
could be developed or learned (e.g., increasing calculated risk taking, possibly
through statistical education). This result supports previous research by DeDonno
and Detterman (2008) and Dixon and Jackson (2008), who found that poker training
increased poker performance. Thus, the second implication of the current study
pertains both to players who desire to improve their poker playing ability or skill level
and to businesses and individuals seeking to aid players in game improvement. Given
the appropriate training, skill increases are likely attainable. The extent to which
poker skill can be increased in general is, however, a topic for future investigations.
Future Directions
An examination of gender differences associated with poker ability should be
undertaken. Nearly half of the current study sample was female and the PSM scores of
females ranged from 9 to 22 (of 35), indicating that the higher PSM scores were not
solely attained by males. Yet skill level did differ between males and females. Given
the sample composition, however, it was beyond the capabilities of the current study to
evaluate the individual differences of male and female players matched by skill level.
Gender differences and similarities in player attributes, therefore, remain unknown.
Some of the individual differences found to be associated with good poker players could
feasibly be enhanced by learning or training and as such, the second research direction
pertains to the investigation of the impact of learning via both training and experience.
Poker-specific statistical understanding, for example, may be developed by immersion
(i.e., experience) or by focused training (i.e., commercially available training programs).
Greater statistical understanding may in turn lead to changes in risk-taking behaviours
during poker playing (e.g., increases in calculated risk taking, the reduction of reckless
risk taking). In the current study, however, no data were collected regarding whether or
not participants had previously engaged in focused training. Playing experience was
significantly related to poker skill, but only in the univariate analysis. Evaluating
experience in terms of years may have attenuated the relationship, or the relationship
may be spurious. Thus, the magnitude of skill increase due to experience and focused
training requires further investigation. It would also be of interest to assess the
similarities and differences between experience and focused training.
References
Badger, S. (n.d.). Smart poker players - Intelligent poker playing. Retrieved from Play
Winning Poker website: http://www.playwinningpoker.com/poker/smart/
63
CHARACTERISTICS OF GOOD POKER PLAYERS
Barrault, S., & Varescon, I. (2013). Impulsive sensation seeking and gambling
practice among a sample of online poker players: Comparison between non
pathological, problem and pathological gamblers. Personality and Individual
Differences,55, 502–507. doi:10.1016/j.paid.2013.04.022
Bellin, A. (2002, January). Tells: The fine art of losing at poker. The Atlantic
Monthly, 104–107.
Blais, A. R., & Weber, E. U. (2006). A Domain-Specific Risk-Taking (DOSPERT)
scale for adult populations. Judgment and Decision Making,1,33–47.
Brown, S., & Mitchell, L. (2010). An observational investigation of poker style and
the five factor personality model. Journal of Gambling Studies,26, 229–234.
doi:10.1007/s10899-009-9161-9
Browne, R. B. (1989). Going on tilt: Frequent poker players and control. Journal of
Gambling Studies,5,3–21. doi:10.1007/BF01022134
Castaldo, J. (2007). No mercy. Canadian Business,80(19). Retrieved from Canadian
Business website: http://int-www.canadianbusiness.com/lifestyle/no-mercy/
Costa, P. T., & McCrae, R. R. (1992a). NEO PI-R. Professional manual. Odessa,
FL: Psychological Assessment Resources.
Costa, P. T., & McCrae, R. R. (1992b). Revised NEO Personality Inventory (NEO
PI-R) and NEO Five-Factor Inventory (NEO-FFI) professional manual. Odessa, FL:
Psychological Assessment Resources.
Dannewitz, H., & Weatherly, J. N. (2007). Investigating the illusion of control in
mildly depressed and nondepressed individuals during video-poker play. The Journal
of Psychology,141, 307–319. doi:10.3200/JRLP.141.3.307-320
DeDonno, M. A., & Detterman, D. K. (2008). Poker is a skill. Gaming Law Review,
12,31–36. doi:10.1089/glr.2008.12105
Della Sala, S., Foley, J. A., Beschin, N., Allerhand, M., & Logie, R. H. (2010).
Assessing dual-task performance using a paper-and-pencil test: Normative data.
Archives of Clinical Neuropsychology,25, 410–419. doi:10.1093/arclin/acq039
Dixon, M. R., & Jackson, J. W. (2008). Using performance feedback to teach video
poker players to gamble better. Analysis of Gambling Behavior,2,49–54.
Ford, M. E., & Tisak, M. S. (1983). A further search for social intelligence.
Journal of Educational Psychology,75, 196–206. doi:10.1037/0022-0663.75.
2.196
64
CHARACTERISTICS OF GOOD POKER PLAYERS
Leonard, C. A., Staples, J., & Williams, R. J. (2014). Development and validation of
the Poker Skills Measure (PSM). Journal of Gambling Studies. Advance online
publication. doi:10.1007/s10899-014-9475-0
Linnet, J., Frøslev, M., Ramsgaard, S., Gebauer, L., Mouridsen, K., & Wohlert, V.
(2012). Impaired probability estimation and decision-making in pathological
gambling poker players. Journal of Gambling Studies,28, 113–122. doi:10.1007/
s10899-011-9244-2
Linnet, J., Gebauer, L., Shaffer, H., Mouridsen, K., & Møller, A. (2010).
Experienced poker players differ from inexperienced poker players in estimation
bias and decision bias. Journal of Gambling Issues,24,86–100. doi:10.4309/
jgi.2010.24.6
MacKay, T. (2012). Problem gambling risk factors in Internet and non-Internet
gamblers (Doctoral dissertation, University of Calgary, Calgary, Alberta, Canada).
Retrieved from http://hdl.handle.net/11023/176
Martinez, T. M., & Lafranchi, R. (1969). Why people play poker. Society,6,30–35.
doi:10.1007/BF02819882
McCormack, A., & Griffiths, M. D. (2011). What differentiates professional poker
players from recreational poker players? A qualitative interview study. International
Journal of Mental Health and Addiction,10, 243–257. doi:10.1007/s11469-011-9312-y
Meinz, E. J., Hambrick, D. Z., Hawkins, C. B., Gillings, A. K., Meyer, B. E., &
Schneider, J. L. (2012). Roles of domain knowledge and working memory capacity
in components of skill in Texas Hold’Em poker. Journal of Applied Research in
Memory and Cognition,1,34–40. doi:10.1016/j.jarmac.2011.11.001
Mitrovic, D., & Brown, J. (2009). Poker mania and problem gambling: A study of
distorted cognitions, motivation and alexithymia. Journal of Gambling Studies,25,
489–502. doi:10.1007/s10899-009-9140-1
No-Limit Hold’em Cash Game (Version 1) [Computer software]. (2011). Tucson,
AZ: Wilson Software.
Oliveira, M. P. M. T., & Silva, M. T. A. S. (2001). A comparison of horse-race,
bingo, and video poker gamblers in Brazilian gambling settings. Journal of Gambling
Studies,17, 137–149. doi:10.1023/A:1016672630220
Palomäki, J., Laakasuo, M., & Salmela, M. (2013). ‘‘ Don’t worry, it’s just poker!
‘‘ —Experience, self-rumination and self-reflection as determinants of decision-
making in on-line poker. Journal of Gambling Studies,29, 491–505. doi:10.1007/
s10899-012-9311-3
65
CHARACTERISTICS OF GOOD POKER PLAYERS
Pinson, H. (n.d.). Poker success –Three traits of a successful poker player. Retrieved
from Poker Vibe website: http://www.poker-vibe.com/poker/success/
Potter van Loon, R. J. D., van den Assem, M. J., & van Dolder, D. (2015). Beyond
chance? The persistence of performance in online poker. PLOS ONE, 10, e0115479.
doi:10.1371/journal.pone.0115479
Sattler, J. M. (1988). Assessment of children (3rd ed.). San Diego, CA: Author.
Schlicht, E. J., Shimojo, S., Camerer, C. F., Battaglia, P., & Nakayama, K. (2010).
Human wagering behavior depends on opponents’faces. PLOS ONE,5, e11663.
doi:10.1371/journal.pone.0011663
Shead, W. N., Hodgins, D. C., & Scharf, D. (2008). Differences between poker
players and non-poker-playing gamblers. International Gambling Studies,8, 167–178.
doi:10.1080/14459790802139991
Siler, K. (2010). Social and psychological challenges of poker. Journal of Gambling
Studies,26, 401–420. doi:10.1007/s10899-009-9168-2
Silvera, D. H., Martinussen, M., & Dahl, T. I. (2001). The Tromsø Social
Intelligence Scale, a self-report measure of social intelligence. Scandinavian Journal
of Psychology,42, 313–319. doi:10.1111/1467-9450.00242
Slepian, M. L., Young, S. G., Rutchick, A. M., & Ambady, N. (2013). Quality of
professional players’poker hands is perceived accurately from arm motions.
Psychological Science,24, 2335–2338. doi:10.1177/0956797613487384
St. Germain, J., & Tenenbaum, G. (2011). Decision-making and thought
processes among poker players. High Ability Studies,22,3–17. doi:10.1080/
13598139.2011.576084
Tayfun, D., & Cetin, B. (2009). The validity, reliability and factorial structure of the
Turkish version of the Tromso Social Intelligence Scale. Educational Sciences:
Theory & Practice,9(2), 709–720.
Thorndike, R. L., Hagen, E. P., & Sattler, J. M. (1986). The Stanford-Binet
Intelligence Scale: Fourth Edition, Guide for administering and scoring. Chicago, IL:
Riverside Publishing.
Weber, E. U., Blais, A., & Betz, N. E. (2002). A domain-specific risk-attitude scale:
Measuring risk perceptions and risk behaviors. Journal of Behavioral Decision
Making,15, 263–290. doi:10.1002/bdm.414
Wechsler, D. (1944). The measurement of adult intelligence (3rd ed.). Baltimore, MD:
Williams & Wilkins.
66
CHARACTERISTICS OF GOOD POKER PLAYERS
Williams, R. J. (2003). Reliability and validity of four scales to assess gambling
attitudes, gambling knowledge, gambling fallacies and ability to calculate gambling
odds. Unpublished technical report, University of Lethbridge, Lethbridge, Alberta,
Canada.
Williams, R. J., & Volberg, R. A. (2010). Best practices in the population assessment
of problem gambling. Guelph, ON: Ontario Problem Gambling Research Center.
Williams, R. J., & Volberg, R. A. (2014). The classification accuracy of four problem
gambling assessment instruments in population research. International Gambling
Studies,14,15–28.
Wilson, C. (2003). Raise you 50. New Scientist,180(2426–2428), 64–67.
*******
Submitted March 7, 2014; accepted December 9, 2014. This article was peer-
reviewed. All URLs were available at the time of submission.
For correspondence: Carrie A. Leonard, M.Sc., C885 University Hall, Department
of Psychology, 4401 University Drive, University of Lethbridge, Lethbridge,
AlbertaT1K 3M4, Canada. E-mail: leonardcarrieann@gmail.com
Competing interests: None declared.
Ethics approval: The University Lethbridge Human Subject Research Committee
Acknowledgements: This research was conducted with funding assistance to the first
author from the Alberta Gambling Research Institute (AGRI) and the Social
Sciences and Humanities Research Council (SSHRC).
67
CHARACTERISTICS OF GOOD POKER PLAYERS
