Welcome to Week 6 of my Genius
Hour Project on how math and stats are implicit when betting on sports. I hope
that you all had an awesome Christmas break, as I know everyone deserved some
rest and time to themselves, especially during these difficult times. My main
goal for this week was to learn more about statistics in general, and how they
can help people in both daily life and in work! I also wanted to research some
terms and concepts related to statistics, and freshen up on what I have already
learned in the subject area.
The field of statistics is
crucial to understand as a sports bettor, Bookmaker, worker, or even the
average person. One common, everyday example of the importance of statistics pertains
to weather. Do you ever wonder how the forecast is predicted? Well, it all stems
from computer models that compare prior weather with the current weather to
make a calculation. Another way that statistics are prevalent on a daily basis
is when we simply make a prediction. For instance, when we set our alarm clock,
we are predicting that it will go off at the exact time in the morning
for which we have set it. In other words, we are using the statistic that our
alarm almost always sounds when we have set it. Statistics are also used
by insurance companies. When you pay those high insurance fees on your
car, house, or medicine, these rates are calculated via statistical models that
determine your relative risk.
A concept that I came across,
which I feel is vital for my students to grasp, relates to statistical bias and
misleading statistics. These include strategies and methods that companies use
to misinform, and even deceive, their consumers. Often, this trickery goes
unnoticed, which allows the company to benefit at the peril of the consumers. There
are many types of misleading statistics, and all are important for any consumer
to realize. I will list some of the main types that the government, casinos,
stores, agencies, organizations, and other companies use. For sake of this
Genius Hour, I will use betting and bookmaking to provide some examples:
1)
Faulty Polling: when
a question is phrased in a persuasive manner
· Example:
“Do you believe that casinos share the public interest, since they give away so
much money” sounds like casinos “give out” money and require additional government
funding
2)
Flawed Correlations: measuring
many variables until, eventually, a random correlation appears
· Example:
a researcher measures so many variables that they finally find a correlation
between (A) an increase of slot machine wins in September in New York and (B)
an increase in employment in September in New York
3)
Misleading Visualization: graphs
and charts that are misleading
· Example:
one scale in a bar graph is twice as big as the other, even though the actual
percentage of this variable is only half
4) 
Selective Bias: a specific
sample of people is surveyed, or omitted, in an attempt to influence the data
collection
· Example:
asking a college fraternity what the legal gambling age should be, versus
asking a group of “OLG Play Safe” volunteers
5) 
Small Sample Size: using percentage
change as an indicator, after asking a small number of people
· Example:
asking a sample of 10 people if they win at the casino, where 8 answer “yes”, does
not necessarily mean that 80% of the population wins at the casino
It is imperative that I, as a
mathematical educator and role model, teach my students how to identify and
combat such fraudulent mathematical data. Not only will this assist youth when
they are gambling and making purchases, but it will also benefit them as they
navigate through life, in general!
For next week’s hour (or more),
I will be gaining a bettor’s perspective on the topic. I already have one, so
it will be interesting to broaden this point of view and learn more about how
to educate my students!
References
Lebied, M. (2018). Misleading
statistics examples – discover the Potential for misuse of statistics & data
in the digital age. Data Analysis. https://www.datapine.com/blog/misleading-statistics-and-data/
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