Texas Mathematics Teacher Spring/Summer 2022 - 12

An Odyssey with Time Series: The Masters Golf Tournament
The charts tell us even more that could easily be missed without them! For example, the Stage 3 moving range chart
shows a signal from 1965 to 1966. Table 1 reports that Jack Nicholas was the champion in both years, but his winning
score was 271 stokes in 1965, yet 288 stokes in 1966. That is a curious 17 stroke loss from one year to the next for arguably
one of golf's greatest players, even though he was the champion in both years! Although not traversing the upper limit of
289.12, the Stage 4 individuals chart depicts a high winning score of 289 strokes for Zack Johnson in 2007. His 289-stroke
winning score is matched much earlier by Sam Snead in 1954 and Jack Burke, Jr. in 1956. We include summary Tables 5a
and 5b only for completeness and general interest.
Table 5a. Masters tournament 5 lowest and 5 highest winning scores
Stephen M. Scariano, Ph.D
sms049@shsu.edu
Professor of Mathematics & Statistics
Sam Houston State University
Table 5b. Frequency for winning champions with 3 or more wins
Conclusion
Neither of the authors is a golf enthusiast, and we have
never once participated in the sport itself. Nonetheless,
we recognize that real sports data are likely to spark
the interest of students studying time series analysis for
the first time. For instance, the conclusion of the 2021
Summer Olympics has now given the world new world
athletic records, particularly in women's track and field
events. Such results are just the most recent observations
available for time series analysis for any Olympic sports
that tease one's interest. Moreover, past Olympic data
are widely available and easily accumulated from many
reliable sources, including the internet. So, there are ample
opportunities for students to investigate, collect, plot and
analyze sports time series data.
Time series analysis is oftentimes a difficult endeavor,
and some time series are so ill-behaved that moving range
and individuals charts are inappropriate, if not altogether
useless, in those contexts. For example, a reasonable time
series analysis of the cyclic nature of new COVID-19 infections
would defy the use of moving range and individuals
charts in favor of more statistically, sophisticated methods.
Still, many naturally occurring time series are amenable
to the tools presented here, which should be added to
your students' statistical " toolkits. "
12 | Spring/Summer 2022
Ananda B. W. Manage, Ph.D
wxb001@shsu.edu
Professor of Statistics
Sam Houston State University
References:
Masters Tournament. (2021). The Masters Past Winners.
https://www.masters.com/en_US/tournament
past_winners.html.
Minitab 17 Statistical Software. (2010).
[Computer software]. Minitab, Inc.
http://www.minitab.com
Montgomery, D. C. (2013). Introduction to statistical
quality control. J. Wiley & Sons.
Shewhart, W. A. (1999). Economic control of quality of
manufactured product. American Society
for Quality.
Wheeler, D. J. (2000). Understanding variation: The key to
managing chaos. SPC Press.
Texas Mathematics Teacher
https://www.masters.com/en_US/tournament/past_winners.html https://www.masters.com/en_US/tournament/past_winners.html http://www.minitab.com

Texas Mathematics Teacher Spring/Summer 2022

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http://www.brightcopy.net/allen/txmt/68-01
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http://www.brightcopy.net/allen/txmt/66-02
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