Predicting Team Advancement in Major League Baseball Postseason
Using Borda Count
CHIH-CHENG CHEN
Department of Sport Management
Aletheia University
32, Zhen-Li St., Tamsui Dist., New Taipei City 25103
TAIWAN
TIAN-SHAING KUO
Department of Regimen and Leisure Management
Tainan University of Technology
529, Zhongzheng Rd., Yongkang District, Tainan City 710302
TAIWAN
KUANG-TSAN HUNG
Department of Event Management
HungKuo DeLin Institute of Technology
1, Ln. 380, Qingyun Rd., Tucheng Dist., New Taipei City 236302
TAIWAN
CHUNG-YU TSAI
Department of Sport Management
Aletheia University
32, Zhen-Li St., Tamsui Dist., New Taipei City 25103
TAIWAN
MING-YAO CHEN*
Department of Sports Information & Communication
Aletheia University
32, Zhen-Li St., Tamsui Dist., New Taipei City 25103
TAIWAN
*corresponding author
International Journal of Applied Sciences & Development
DOI: 10.37394/232029.2024.3.2
Chih-Cheng Chen, Tian-Shaing Kuo,
Kuang-Tsan Hung, Chung-Yu Tsai, Ming-Yao Chen
E-ISSN: 2945-0454
12
Volume 3, 2024
Abstract:- The prediction of sports competition outcomes has long been a topic of interest in academia and
among sports enthusiasts. This study focuses on Major League Baseball (MLB) as its research subject,
encompassing the years 2020, 2021, and 2022. By employing a set of established evaluation criteria, comprising
five pitching and five hitting indicators from previous literature, the regular-season performance of the 30 MLB
teams across both leagues (National League and American League) over the three-year period was compiled.
Subsequently, a data normalization technique combined with the Borda count concept was proposed to develop
a model for forecasting team advancement in the postseason. The predictive accuracy of the model presented in
this study for determining MLB postseason qualifiers from 2020 to 2022 fell within the range of 55.6% to
66.7%, akin to models utilizing extensive datasets. Notably, the proposed model is more comprehensible and
user-friendly, offering ease of understanding and application for sports enthusiasts and facilitating its potential
utilization and dissemination in the sporting community.
Key-words: - short-term sporting events, predicting models, normalization
Received: July 19, 2023. Revised: November 26, 2023. Accepted: January 27, 2024. Published: March 26, 2024.
1 Introduction
Predicting the outcomes of sporting events is a
highly sought-after topic among sports enthusiasts.
However, typical sports fans can only access team or
player performance information from official
websites of major sporting events. They rely on
intuition or win-loss records to assess the outcome of
the next match. Research related to predicting sports
event outcomes can be broadly categorized into two
main types. One category focuses on predicting
individual performance of professional sports teams
[1], while the other category involves predicting team
performance [2-3]. However, most studies in this
field rely on extensive data for prediction. For
example, Bailey, Loeppky, and Swartz [1] utilized
logistic regression analysis to predict the batting
average of hitters in the Major League Baseball
(MLB), using data from over 30 teams and more than
162 games per season (over 2,400 games), which
generated over 30 batting indicators per game. On the
other hand, Jia et al. [3] collected over 2 million
records of MLB pitching and hitting data from 2007
to 2011 using artificial intelligence (AI) and machine
learning techniques, aiming to predict game results
for the 2012 season. Huang and Li [2] also employed
deep learning techniques, a form of AI, to predict
MLB game outcomes based on information gathered
from a total of 4,858 games in the 2019 season.
However, many sporting events do not have as many
matches or accumulate a large volume of game data,
making it less convenient to analyze them using big
data methods. For instance, the MLB postseason,
which garners significant attention from baseball
enthusiasts worldwide, lasts approximately one
month and consists of 28 to 45 matches, which is
significantly fewer compared to more then 2,000
matches played in a single regular season of MLB.
To address the limitation of limited data, this
study will employ Multi-Criteria Decision Making
(MCDM) methods, commonly utilized for evaluation,
ranking, and selection across various domains, to
predict the outcomes of short-term sporting events.
MCDM methods have been frequently applied in
research related to sports [4-5], including prediction
studies [6-7]. The Borda count (a kind of MCDM
method) is dvoting method employed to rank or rate
options [8-9]. In the context of predicting sports event
outcomes, it can be applied in various domains such
International Journal of Applied Sciences & Development
DOI: 10.37394/232029.2024.3.2
Chih-Cheng Chen, Tian-Shaing Kuo,
Kuang-Tsan Hung, Chung-Yu Tsai, Ming-Yao Chen
E-ISSN: 2945-0454
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Volume 3, 2024
as ranking teams or athletes and forecasting match
rankings [10-11].
Drawing upon the aforementioned information,
the fundamental aim of this research is to utilize the
Borda count methodology. Through a comprehensive
analysis of MLB teams' performance throughout the
regular season, the study endeavors to predict the
likelihood of these teams progressing to the
postseason. Moreover, it strives to evaluate their
prospects for triumph in the playoffs by scrutinizing
their matchups against their respective opponents.
2 Methods
2.1 Data
The study to collect the MLB 2020 to 2022
statistics data of regular season by the MLB official
website (mlb.com) which provides game stats. In the
data applied, the technical variables correspond to
values of the teams, not to the individual values of the
players. All the ten criteria (technical variables) were
used [3-4] as criteria including earned run average
(ERA), walks and hits per inning pitched (WHIP),
batting average against (AVG), strkeouts per 9 IP
(SO/9), walks per 9 IP (BB/9), runs batted in (RBI),
batting average (HAVG), on-base percentage (OBP),
slugging percentage (SLG), at bats per home run
(AB/HR). Before using Borda Score, we used
regression analysis in which Wins were the
dependent variable, and ERA, WHIP, AVG, SO/9,
BB/9, RBI, HAVG, OBP, SLG and AB/HR were
independent variables. The analysis results showed
that the R-squared values were 0.974. It means that
the technical variables cited in this study have good
explanatory power for Wins.
2.2 Board count
The Borda count is a voting method employed
to rank or rate options. In the context of predicting
sports event outcomes, it can be applied in various
domains such as ranking teams or athletes and
forecasting match rankings[8-12]. In order to analyse
the data, we conducted normalization proceduce and
Borda count to determine each MLB team’s
performance. There are multistep procedure that
provides a comprehensive ranking system for sets of
data. Steps for Board count as follows:
Step 1. criteria normalization
There are three different methods of normalizing the
sequences including benefit type, defect type, and
target type methods [13]. In this study, the benefit and
defect method are used.
The benefit type (SO/9, RBI, HAVG, OBP and SLG)
method indicates that the larger target value is better.
The calculation is as follows:
The defect type (ERA, WHIP, AVG, BB/9, AB/HR)
method indicates that the smaller target value is better.
The calculation is as follows:
(2)
Where is the largest value in criteria j
and is the smallest value in criteria j.
Step 2 Borda count calculation
By applying normalization techniques, the
performance data of each team across ten criteria is
transformed into a range of 0 to 1. This normalized
data is then multiplied by 30 (as there are 30 teams in
MLB). Finally, the sum of the performance scores for
each criterion is calculated for each team. The higher
Borda count is better. The calculation is as follows:
Borda count of
(3)
(1)
Where is the largest value in criteria j
and is the smallest value in criteria j.
International Journal of Applied Sciences & Development
DOI: 10.37394/232029.2024.3.2
Chih-Cheng Chen, Tian-Shaing Kuo,
Kuang-Tsan Hung, Chung-Yu Tsai, Ming-Yao Chen
E-ISSN: 2945-0454
14
Volume 3, 2024
3 Results
3.1 Performance of MLB Teams for
Advancement in the Postseason
Since 2012, the MLB postseason consists of
eight teams, comprising three division champions
from each league and two wild card teams. The MLB
has organized the postseason into three rounds of
competition: the Division Series, League
Championship Series, and World Series. The
Division Series employs a "best of 5" format, while
the subsequent rounds utilize a "best of 7" format.
During the Division Series, the team with the best
record in a league faces off against the wild card team.
In cases where both teams belong to the same
division within the league, the division champion
with the second-best record competes against the
wild card team. Notably, the team with the superior
regular-season record enjoys the home-field
advantage during the series, while the wild card team
is not granted such a benefit, except during the World
Series.
In 2022, MLB increased the postseason
excitement by adding three wild card teams to each
league, making a total of 12 teams in the playoffs.
This transformed the postseason format from three
rounds to four, including the new Wild Card Series.
In this "best of 3" format, three division champions
with weaker regular-season records and the wild card
team compete for a spot in the Division Series. The
winner then faces the top two divisional teams.
Division champions have the home-field advantage,
and the winner advances to the League
Championship Series.
In this study, we used the data from MLB regular
seasons 2020 to 2022. By employing equations (1) to
(3), the Borda count for each team in the current year
is calculated. A higher Borda count value signifies a
stronger team with a higher probability of winning
during matches.
3.1.1 2020 MLB postseason
In 2020, due to the impact of Covid-19, the
regular season games were reduced to 60.
Additionally, the qualifying rules for the postseason
were modified. In the 2020 playoffs, each league had
8 teams advancing to the postseason, comprising the
top two teams from each of the three divisions (6
teams in total) and 2 wild card teams. This resulted in
a total of 16 teams across both leagues, competing in
a four-round format.
Table 1.
Borda count and ranking of MLB teams in the
2020 postseason
TEAM
League
Borda count
Ranking
New York Yankees (X)
AL
207
4
Chicago White Sox (W)
AL
200
5
Minnesota Twins (X)
AL
189
6
Tampa Bay Rays (X^)
AL
188
7
Cleveland Indians (Y)
AL
168
8
Oakland Athletics (X)
AL
158
10
Toronto Blue Jays (W)
AL
147
13
Houston Astros (Y)
AL
139
14
Los Angeles Dodgers (X^)
NL
268
1
San Diego Padres (Y)
NL
226
2
Atlanta Braves (X)
NL
208
3
Cincinnati Reds (W)
NL
165
9
Milwaukee Brewers (W)
NL
154
11
Chicago Cubs (X)
NL
148
12
St. Louis Cardinals (Y)
NL
130
15
Miami Marlins (Y)
NL
92
16
Note: X is Division Champion, Y is Division
second place, W is Wild Card, ^ is most wins of
league, AL is American League, NL is National
League
Among the teams that qualified for the playoffs
in 2020, the top three performers in the National
League (NL) were the Los Angeles Dodgers, San
International Journal of Applied Sciences & Development
DOI: 10.37394/232029.2024.3.2
Chih-Cheng Chen, Tian-Shaing Kuo,
Kuang-Tsan Hung, Chung-Yu Tsai, Ming-Yao Chen
E-ISSN: 2945-0454
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Volume 3, 2024
Diego Padres, and Atlanta Braves, as illustrated in
Table 1. Meanwhile, in the American League (AL),
the top three teams were the New York Yankees,
Chicago White Sox, and Minnesota Twins.
3.1.2 2021 MLB postseason
In 2021, MLB reverted to its original playoff
format, where apart from the divisional champions,
the two best-performing teams in each league also
qualified for the postseason as wild card teams. As a
result, a total of five teams from each league secured
playoff berths. According to the data presented in
Table 2, the two top-performing teams in the National
League were the Los Angeles Dodgers and San
Francisco Giants. In the American League, the best-
performing teams were the Houston Astros and
Chicago White Sox.
Table 2.
Borda count and ranking of MLB teams
in the 2021 postseason
TEAM
League
Borda
count
Ranking
Houston Astros (X)
AL
227
3
Chicago White Sox (X)
AL
221
4
Tampa Bay Rays (X^)
AL
212
5
Boston Red Sox (W)
AL
187
7
New York Yankees (W)
AL
183
8
Los Angeles Dodgers (W)
NL
246
1
San Francisco Giants (X^)
NL
229
2
Atlanta Braves (X)
NL
188
6
Milwaukee Brewers (X)
NL
181
9
St. Louis Cardinals (W)
NL
127
10
Note: X is Division Champion, W is Wild
Card, ^ is most wins of league, AL is
American League, NL is National League
3.1.3 2022 MLB postseason
Starting from 2022, MLB made further
modifications to the playoff advancement regulations.
The number of wild card teams was increased from
two to three in each league, resulting in an additional
playoff berth for each league. The playoff format was
also altered, shifting from the divisional champions
directly advancing to the division series to the
divisional champion with the worst record beginning
their postseason journey from the wild card stage.
Based on the data provided in Table 3, the two top-
performing teams were the Los Angeles Dodgers in
the National League and the Houston Astros in the
American League. The third and fourth positions
were secured by the New York Yankees (American
League) and the Atlanta Braves (National League),
respectively.
Table 3.
Borda count and ranking of MLB teams
in the 2022 postseason
TEAM
League
Borda
count
Ranking
Houston Astros (X^)
AL
243
2
New York Yankees (X)
AL
238
3
Toronto Blue Jays (W)
AL
217
6
Cleveland Guardians (X)
AL
178
8
Seattle Mariners (W)
AL
175
10
Tampa Bay Rays (W)
AL
172
12
Los Angeles Dodgers (X^)
NL
279
1
Atlanta Braves (X)
NL
233
4
New York Mets (W)
NL
229
5
Philadelphia Phillies (W)
NL
193
7
St. Louis Cardinals (X)
NL
175
9
San Diego Padres (W)
NL
174
11
Note: X is Division Champion, W is Wild
Card, ^ is most wins of league, AL is
American League, NL is National League
3.2 Predicting Accuracy in the MLB
Postseason
In this study, we utilize the Borda count of teams
that entered the playoffs from 2020 to 2022 as a
criterion for assessing their advancement during the
respective years. The results are presented in the
following section.
3.2.1 Predicting Accuracy of 2020 MLB
postseason
According to the data presented in Table 4, the
Borda count proposed in this study correctly
predicted the winning team in five out of eight
matchups during the 2020 MLB Wild Card stage.
Moving on to the Divisional stage, it accurately
International Journal of Applied Sciences & Development
DOI: 10.37394/232029.2024.3.2
Chih-Cheng Chen, Tian-Shaing Kuo,
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predicted two out of four matchups.
Table 4.
2020 MLB Postseason Predictions
Series
Wild Card
Division
League
World
BC
P
A
J
P
A
J
P
A
J
P
A
J
TB
188
TOR
147
TB
TB
1
CLE
168
NYY
NYY
1
NYY
207
NYY
TB
0
MIN
189
OAK
HOU
0
HOU
139
MIN
HOU
0
OAK
158
CWS
OAK
0
CWS
200
TB
TB
1
LAD
LAD
1
LAD
268
LAD
LAD
1
MIL
154
LAD
LAD
1
SD
226
SD
SD
1
STL
130
LAD
LAD
1
CHC
148
ATL
ATL
1
MIA
92
CHC
MIA
0
ATL
208
ATL
ATL
1
CIN
165
Correct ratio is 66.7%
Note: BC is Borda count, P is predict, A is actual, J is Judge, 1
is correct, 0 is incorrect, TB is Tampa Bay Rays, TOR is
Toronto Blue Jays, CLE is Cleveland Indians, NYY is New
York Yankees, MIN is Minnesota Twins, HOU is Houston
Astros, OAK is Oakland Athletics, CWS is Chicago White Sox,
LAD is Los Angeles Dodgers, MIL is Milwaukee Brewers, SD
is San Diego Padres, STL is St. Louis Cardinals, CHC is
Chicago Cubs, MIA is Miami Marlins, ATL is Atlanta Braves,
CIN is Cincinnati Reds.
Subsequently, it correctly predicted the winning
team in both the League Championship and World
Series matchups. The team ranked first in the Borda
count for that year, the Los Angeles Dodgers, went on
to win the World Series championship. Overall, the
Borda count model proposed in this research
achieved a correct prediction rate of 66.7% across all
15 matchups analyzed.
3.2.2 Predicting Accuracy of 2021 MLB
postseason
Table 5 presents the data on the Borda count of
each team and the outcomes of the matchups during
the 2021 MLB postseason. Utilizing the Borda count
proposed in this study, we achieved correct
predictions for the winning teams in both of the 2021
MLB Wild Card stage matchups.
Table 5.
2021 MLB Postseason Predictions
Series
Wild Card
Disision
League
World
BC
P
A
J
P
A
J
P
A
J
P
A
J
BOS
187
NNY
183
BOS
BOS
1
TB
212
TB
BOS
0
HOU
227
HOU
HOU
1
HOU
HOU
1
CWS
221
LAD
246
HOU
ATL
0
STL
127
LAD
LAD
1
SF
229
LAD
LAD
1
LAD
ATL
0
MIL
181
ATL
ATL
1
ATL
188
Correct ratio is 55.6%
Note: BC is Borda count, P is predict, A is actual, J is Judge, 1
is correct, 0 is incorrect, BOS is Boston Red Sox, NYY is New
York Yankees, TB is Tampa Bay Rays, HOU is Houston
Astros, CWS is Chicago White Sox, LAD is Los Angeles
Dodgers, SF is San Francisco Giant, MIL is Milwaukee
Brewers, ATL is Atlanta Braves.
Moving on to the Divisional stage, we
accurately predicted three out of four matchups.
Subsequently, in the League Championship, we
correctly predicted the winning team in one out of
two matchups. Finally, in the World Series matchup,
we made an accurate prediction for the winning team.
The team with the highest Borda count in 2021, the
Atlanta Braves, emerged as the World Series
champions. Overall, the Borda count model proposed
in this research achieved a correct prediction rate of
55.6% across all nine matchups analyzed in the 2021
MLB postseason.
3.2.3 Predicting Accuracy of 2022 MLB
postseason
According to the data presented in Table 6, the
Borda count proposed in this study correctly
predicted the winning team in two out of four
matchups during the 2022 MLB Wild Card stage.
International Journal of Applied Sciences & Development
DOI: 10.37394/232029.2024.3.2
Chih-Cheng Chen, Tian-Shaing Kuo,
Kuang-Tsan Hung, Chung-Yu Tsai, Ming-Yao Chen
E-ISSN: 2945-0454
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Volume 3, 2024
Table 6.
2022 MLB Postseason Predictions
Series
Wild Card
Disision
League
World
BC
P
A
J
P
A
J
P
A
J
P
A
J
TOR
217
SEA
175
TOR
SEA
0
HOU
243
HOU
HOU
1
CLE
178
HOU
HOU
1
TB
172
CLE
CLE
1
NYY
238
NYY
NYY
1
NYM
229
HOU
HOU
1
SD
174
NYM
SD
0
LAD
279
LAD
SD
0
STL
175
PHI
193
PHI
PHI
1
PHI
PHI
1
ATL
233
ATL
PHI
0
Correct ratio is 63.6%
Note: BC is Borda count, P is predict, A is actual, J is
Judge, 1 is correct, 0 is incorrect, TOR is Toronto Blue
Jays, SEA is Seattle Mariners, HOU is Houston
Astros, CLE is Cleveland Guardians, TB is Tampa Bay
Rays, NYY is New York Yankees, NYM is New York
Mets, SD is San Diego Padres, LAD is Los Angeles
Dodgers, STL is St. Louis Cardinals, PHI is
Philadelphia Phillies, ATL is Atlanta Braves.
Moving on to the Divisional stage, it also
accurately predicted the winning team in two out of
four matchups. Subsequently, in the League
Championship and the World Series matchups, the
Borda count correctly predicted the winning team in
all three matchups. The team with the highest Borda
count in 2022, the Houston Astros, emerged as the
World Series champions. In general, the study applied
the Borda account model to predict rate reached
63.6% in all eleven matches of the 2022 MLB
postseason.
4 Discussion
Researchers of sport management and bettors
are always interested in the way of prediction of
matches. However, the study applied the Borda
account model to predict the advance of postseason
MLB throughout the regular-season by calculating
the winning probabilities. According to the results
obtained in this research, the prediction accuracy for
the postseason from 2020 to 2022 fell within the
range of 55.6% to 66.7%. These results closely
resemble the accuracy achieved by Huang and Li [2]
and Jia et al.[3] in their utilization of extensive data
and algorithms to predict game outcomes. For an
extended period, predicting the results of baseball
games has been a topic of great interest for many
scholars and fans. They rely on various team
performance indicators to forecast match outcomes,
with many methods yielding prediction accuracies of
approximately 50%. As a result, the method proposed
in this study, which relies on limited data to predict
baseball game results, demonstrates commendable
accuracy and can be considered an effective approach.
Predicting game outcomes has always been a
highly regarded and popular topic, but most studies
tend to focus on longer events with numerous games,
generating large amounts of data. However, short-
term events also hold their appeal, such as
professional league playoffs, FIFA World Cup, or the
Baseball World Cup, which are significant
International tournaments. Therefore, this research
proposes a method that allows individuals to access
data and employ computational techniques to predict
the results of such events.
The results of this study demonstrate that the
application of Borda count yields a good predictive
capability for determining the winning ability of each
participating team, thus offering valuable insights for
predicting team advancements in various sporting
events.
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Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
The authors equally contributed in the present
research, at all stages from the formulation of the
problem to the final findings and solution.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
No funding was received for conducting this study.
Conflict of Interest
The authors have no conflicts of interest to declare
that are relevant to the content of this article.
Creative Commons Attribution License 4.0
(Attribution 4.0 International, CC BY 4.0)
This article is published under the terms of the
Creative Commons Attribution License 4.0
https://creativecommons.org/licenses/by/4.0/deed.en
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International Journal of Applied Sciences & Development
DOI: 10.37394/232029.2024.3.2
Chih-Cheng Chen, Tian-Shaing Kuo,
Kuang-Tsan Hung, Chung-Yu Tsai, Ming-Yao Chen
E-ISSN: 2945-0454
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