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内容概要:本文构建了一个综合评估模型来评选过去一百年(1900年至现在)的最佳大学体育教练。首先确定了评估指标体系,如胜率、稳定性、冠军数量、个人荣誉、点击率等,并通过网络工具将抽象概念转化为量化值。接着提出两种时间线偏差补偿模型——线性拟合模型和加权求和模型,修正Google趋势统计数据。其次,利用层次分析法(AHP)和灰关联分析(GRA)及最大熵模型(Maxent),计算主观权重和客观权重,最后合成综合权重进行最终排序。通过对模型的敏感性和通用性的测试验证了其稳定性和广泛性。 适合人群:对大学体育、数据建模及评估方法感兴趣的体育爱好者、研究者和技术人员。 使用场景及目标:帮助理解和选择过去百年的最佳大学体育教练。通过数学模型提供一种更为客观和科学的方法,降低主观因素的影响。可用于指导教练选拔、团队建设和运动发展等方面的工作。 其他说明:文章提到的研究不仅适用于足球、篮球、棒球等领域,还可以应用于其他类型的竞技项目。
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For office use only
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Team Control
Number
26911
Problem Chosen
B
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Who is the Centennial Best Coach?
In this paper, we present a three-stage comprehensive
evaluation model. Firstly, in the light of the common sense and
logical analysis, we deduce a detailed Assessment Metrics set
(Winning Percentage, Stability, Championship Number, Personal
Awards, Clicking Rate, Players’ Honors, Draftees by the
professional league) and convert some abstract concepts to
quantized and concrete values by utilizing unique methods
including Google Trends to aid in our analysis. Then to simplify
the following work, we filter the coach candidates according to
the shared characteristics of excellent coaches and modify the
index value with various methods. What’s more, as to the
timeline deviation, two modification models are proposed for
our google trends statistics, a linear-fitting model and a
weighted-sum model included.
Secondly, a model combining Analytical Hierarchy Process
(AHP) and a novel method called Max Entropy Model (Maxent)
based on Grey Relation Analysis (GRA)is applied in order to
calculate both the subjective and objective weights. In our model,
AHP diminishes the possible deviation of subjective weights, and
Maxent based on GRA renders an insight into the intrinsic
statistics and objective weights concerned are in turn provided.
In the final stage, we combine both weights mentioned above
together to obtain the final comprehensive weights and in turn
render the final ranking. According to relevant documents, our
ranking results are fairly credible.
Besides, we apply sensitive analysis to our model and test our
model in women’s volleyball. The outcome of the model proves
the robustness and universality in our model.
Key words:
Timeline deviation compensation AHP Maxent GRA
Team # 26911 Page 1 of 19
1 Introduction
When we are sitting on bleachers, cheering for a college football bowl game
held in an immense college stadium, the hero behind the scenes should always
be kept in our heart-the coach. Different coaches display their unique skills in
training and communicating with their players, and, in the meantime, accom-
plish different achievements in their career.
Different people hold distinct standards for the best coach all time. Much
too subjective factors are involved in the personal evaluation of the ’best’. In
this paper, however, we focus on figuring out a more objective and convinc-
ing approach to evaluating the best coach ever in the past century (1900-Now)
.Considering the fact that a large proportion of this problem depends on the
subjective factors, we build the following model in order that some implicit in-
formation lurking in the problem can also be utilized to make the approach more
robust and convincing:
• Step 1:According to the characteristics of the sports, we set the fundamen-
tal assessment metrics in this problem and browse the Internet to obtain
the relevant statistics.
• Step 2:Apply an improved Analytical Hierarchy Process (Synthesis Hier-
archy Process) to this problem and calculate the subjective weights in the
evaluation problem.
• Step 3:Combine Grey Relational Analysis (GRA) Model and Max Entropy
Model (MaXent) in Information Theory to provide a detailed insight into
the objective weights.
• Step 4: Render the comprehensive assessment value of each candidates in
this problem according to the subjective& objective weights and the rele-
vant statistics in this problem and provide the best college coach all time
in turn.
2 Background
American competitive sports are categorized to Professional Sports and A-
mateur Sports. The former is in the charge of each professional clubs while the
latter is mainly under the management of American Olympic Committee (AOC).
American Olympic Committee divides its members into four groups which are
respectively marked by ABCD. The group marked by B consists of the Nation-
al Collegiate Athletic Association (NCAA) which is directly responsible for all
Team # 26911 Page 2 of 19
kinds of intercollegiate games. Owing to the great enthusiasm of the college for
the sports and the large scale of the athletic teams in each school, the NCAA is
thus a considerably enormous organization which is then subdivided into three
divisions and dozens of leagues, where football, basketball, baseball, volleyball
and some track and field games are held annually.[1]
The different location in the divisions is a direct indicator of the level of this
college’s athletic teams. Not only does the division I school pour more invest-
ment to the sports teams but they are also capable of recruiting more potential
players and more excellent coaches in the meantime.
Hence, an assumption can be made about this problem that the division one
school in NCAA has greater strength in the athletic games and symbolizes the
highest level among the American colleges.[2]
3 Model Assumptions and Definitions
3.1 Model Assumptions
• Top five coaches must be in the top divisions of NCAA (Division I for
Baseball and Basketball, Division I-A for Football).
• The searched frequency of a coach on Google is in direct proportion to the
prestige of him (her).
• The searched frequency is a nonlinear function of the current time and the
coach’s retiring time.
• The number of websites returned by the Google search engine concerning
an award name for the coach is in direct ratio with the importance of it.
• All the football championships of the same sport used in our model have
the identical significance.
• All the awards received by the players in one sport all used in our model
to estimate a coachs ability of cultivating players can be seemed equally.
• The number of websites returned by the Google search engine concerning
an award name for the coach is in direct ratio with the importance of it.
3.2 Parameter Definitions
m: Number of the evaluation index;
n: Number of the evaluation object;
Team # 26911 Page 3 of 19
y
i
(k): the k
th
index value of evaluation object i;
x
i
(k): the normalized k
th
index value of evaluation object i;
x
0
(k): the ideal sequence in Grey Relation Analysis
∆
i
(k): Absolute difference
γ(x
i
(k), x
0
(k)): grey relation coefficient
q
i
(k):grey relation depth coefficient
w
j
: the weight of the j
th
index in the second model(GRA&Maxent)
v
j
: the weight of the j
th
index in the first model(AHP)
z
j
: the comprehensive weight of the j
th
index
R
i
: the i
th
coach’s renewed ranking after the weight is altered
Q
i
: the i
th
coach’s ranking before the weight is altered.
EI: absolute entropy increment
SI: relative entropy increment
4 Assessment Metrics
To provide a detailed evaluation mechanism for the best college coach in a
certain sports, we first divide the influencing factor to three main parts, the per-
formance of the teams, the capacity of cultivating & recruiting the players and
the personal prestige of the coach. The three leading factors are then subdivided
into some more detailed indexes.
4.1 Teams’ Performance
Undoubtedly, the prior factor in determining the overall level of a coach is
the professional level of him (her). The most direct indicator of the level is the
professional performance of the team.
4.1.1 Winning percentage in his (her) career
The most explicit indicator of the strength of a team is the statistical record-
s in various competitive games, which in the meantime, can largely reflect the
professional level of this coach. As is widely known, NCAA divides its members
into different divisions and leagues.[2] These diverse divisions and leagues al-
so indicate teams’ diverse levels and strengths in competitive sports. Hence, the
division and the league the college is in should not be taken for granted in this is-
sue. Thus, different weights must be applied to the statistics in the preprocessing
Team # 26911 Page 4 of 19
of the winning percentage according to the Division the team is in. We browse
on the Internet for the top winning percentage records holders.[3][4][5][6][7]
4.1.2 Stability of the team’s performance
The stability of the performance should also be a main concern of us when
analyzing the performance of the team. If a coach cannot keep the good perfor-
mance of the team, but usually leads to great ups and downs of a team, then he
(she) cannot be recognized as the ’best’ as well.[7]
4.1.3 Number of victories in significant games
Because of the special game system of NCAA, different games have diverse
importance. Thus, considerations should be taken into about the difference be-
tween the weight of the games.
4.2 Player’s performance
Great coaches should also have great insight when recruiting the candidates
of the team members, since the main role of the coach is selecting the seed play-
ers for the team and motivating the players.
4.2.1 Professional honors of the players
On the one hand, in competitive sports, some personal honor is also award-
ed to the talented and eye-catching players in the game.[8][9][10][11][12]
On the other hand, another indispensable element in assessing a coach’s
leading ability is number of the players drafted by the professional athletic league
in US.[13][14][15]
4.2.2 Academic Progress Rate(APR)
Despite the great emphasis on the sports teams, the majority of the colleges
are still aimed at improving the overall academic performance of the students,especially
for those Division I schools. Thus, Academic Progress Rate(APR) is recently in-
troduced by NCAA to evaluate the overall level of the player’s academic per-
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