This report will focus on analyzing influential ESPY (Excellence in Sports Performance Yearly Awards) speeches.

When researching text in sports I always was drawn to Jimmy Valvano’s famous speech given at the 1993 ESPYs. This made me want to look into other famous speeches in sports and more specifically the ESPYs. I chose my list of influential ESPY speeches from two separate articles which are USA Today 5 most influential moments in ESPY history and Washington CBS local best speeches in ESPYs history. I tried to use speeches that had transcripts and the recipient speaking. From inspirational speeches of sportscasters with terminal illnesses to the first openly gay NFL player the influence and cultural significance is extremely varied. All of the speeches I ended up choosing were recipients of the Ashe Arthur Award for Courage or the Jimmy V Award for perseverance.

Load Required Packages

library(tidyverse)
library(tidytext)
library(readr)
library(ggthemes)
library(wordcloud2)
library(knitr)

Importing the Text

Here is where the transcripts from each of the speeches are:(https://github.com/ianmyers11/Analysis-of-Inflential-ESPYs-Speeches)

blm_text <- read_csv("text_analysis_txt_files/blm_text.txt", col_names = FALSE)
jenner_text <- read_csv("text_analysis_txt_files/jenner_text.txt", col_names = FALSE)
jimmy_text <- read_csv("text_analysis_txt_files/jimmy_text.txt", col_names = FALSE)
robin_text <- read_csv("text_analysis_txt_files/robin_text.txt", col_names = FALSE)
sam_text <- read_csv("text_analysis_txt_files/sam_text.txt", col_names = FALSE)
scott_text <- read_csv("text_analysis_txt_files/scott_text.txt", col_names = FALSE)

Analysis of Jimmy Valvano’s Speech at the ESPY’s

Video of Jimmy Valvano’s ESPYs Speech

As mentioned before Jimmy Valvano’s speech was my inspiration for analyzing ESPYs speeches. Jimmy Valvano is a famous college basketball coach who is known for leading his underdog North Carolina State basketball team to win the National Championship in 1983. After retiring from basketball, Valvano was diagnosed with metastatic adenocarcinoma, a type of glandular cancer that can spread to the bones. Knowing he did not have long to live the coach committed to spreading the message of hope, love, and persistence. At the inaugural ESPY Awards in 1993 only 9 weeks before his death he gave his famous speech while receiving Arthur Ashe Courage and Humanitarian Award. The speech is known as one of the most motivational speeches ever as he says the famous line if you laugh, you think, and you cry that’s a full day. During the speech, Valvano was visibly in pain but fought through the speech without trouble. In the modern days, the ESPYs now have an award named after him.

Finding the overall sentiment of Jimmy Valvano’s speech

# First step is to remove all of the stop words  
jimmy_text %>% 
  unnest_tokens(word, X1) %>% 
  anti_join(stop_words) -> jimmy_words

# To find the sentiment of the individual words
    # NOTE: excluded work "dick" because it is in reference to a name
jimmy_words %>% 
  filter(!word %in% "dick") %>% 
  inner_join(get_sentiments("afinn")) -> jimmy_sentiment

Finding the highest sentiment words from Jimmy Valvano’s speech

# when finding the highest sentiment words throughout this project the afinn values are kept the same range from -5 to -3 and 3 to 5.
jimmy_sentiment %>% 
  arrange(desc(value)) %>% 
  count(word, value, sort = TRUE) %>% 
  filter(value %in% c("-5", "-4", "-3", "3", "4", "5")) %>% 
  ggplot(aes(word, value, fill=value)) + geom_col() -> plot_jimmy_sentiment

# Extra Visualization Formatting
plot_jimmy_sentiment +
  coord_flip() +
  ggtitle("Highest Sentiment Words from Jimmy Valvano's ESPY Speech") +
  ylab("Value") + 
  theme_bw() -> plot_jimmy

Creating a wordcloud of the most prevalent words in Jimmy Valvano’s speech

jimmy_words %>% 
  group_by(word) %>% 
  count() %>% 
  arrange(desc(n)) %>% 
  wordcloud2(demoFreq, size=1.6, color=rep_len( c("#16193B","#35478C", "#4E7AC7", "#7FB2F0", "#ADD5F7"), nrow(demoFreq))) -> wordcloud_jimmy
word n
i’m 16
that’s 12
day 11
coach 10
lombardi 10
cancer 7
don’t 7
family 6
life 6
people 6

Analysis of the words from Jimmy Valvano’s speech

When looking at Jimmy Valvano’s speech it was interesting to see that most of the highest sentiment words were positive. Words such as wonderful, love, and happiness were integral parts of his motivational speech. When looking at prevalent words the most common word was “i’m” which makes sense as he was talking a lot about himself. Another word that was very prevalent in his speech was “day” which is interesting as in most mentions of the word, Valvano is talking about taking advantage of every day and doing the most with your life.


Analysis of Caitlyn Jenner’s Speech at the ESPYs

Video of Caitlyn Jenner’s ESPYs Speech

Caitlyn Jenner was the recipient of the 2015 Arthur Ashe Courage award for being one of the first openly transgender athletes. Caitlyn is a loved Olympic athlete that transitioned publicly in 2015. In her speech at the ESPYs, she talked a lot about transgender rights and the need to accept people for who they are.

Finding the overall sentiment of Caitlyn Jenner’s speech

jenner_text %>% 
  unnest_tokens(word, X1) %>% 
  anti_join(stop_words) -> jenner_words

# finding the sentiment of the individual words
jenner_words %>% 
  inner_join(get_sentiments("afinn")) -> jenner_sentiment

Finding the highest sentiment words from Caitlyn Jenner’s speech

jenner_sentiment %>% 
  arrange(desc(value)) %>% 
  count(word, value, sort = TRUE) %>% 
  filter(value %in% c("-5", "-4", "-3", "3", "4", "5")) %>% 
  ggplot(aes(word, value, fill=value)) + geom_col() -> plot_jenner_sentiment

# Extra Visualization Formatting
plot_jenner_sentiment +
  coord_flip() +
  ggtitle("Highest Sentiment Words from Caitlyn Jenner's ESPY Speech")+
  xlab('Word') +
  ylab("Value") + 
  theme_bw() -> plot_jenner

Creating a wordcloud of the most prevalent words in Caitlyn Jenner’s speech

jenner_words %>% 
  group_by(word) %>% 
  count() %>% 
  arrange(desc(n)) %>% 
  wordcloud2(demoFreq, size=1.6, color=rep_len( c("#16193B","#35478C", "#4E7AC7", "#7FB2F0", "#ADD5F7"), nrow(demoFreq))) -> wordcloud_jenner
word n
people 12
it’s 8
trans 8
i’m 7
life 7
tonight 6
they’re 5
diane 4
family 4
that’s 4

Analysis of the words from Caityln Jenner’s speech

When looking at the results that I found it was interesting to see that the most prevalent word in the speech was “people.” This word helps to sum up Jenner’s speech as her speech focuses on accepting others and allowing them to be who they are. Another noticeable prevalent word is “trans” as Jenner talked a lot about transgender rights. The highest sentiment words were positive with words such as wonderful, inspiring, and grateful fitting into her core message very well.


Analysis of Robin Robert’s Speech at the ESPYs

Video of Robin Roberts’s ESPYs Speech

Robin Roberts is a television broadcaster and was a sportscaster for ESPN. In 2013, Roberts won the Arthur Ashe Courage Award for her battle with both breast cancer and myelodysplastic syndrome (MDS). Roberts needed a bone marrow transplant which she received. During her speech, she promoted the need for these transplants as well as standing up for beliefs no matter what the cost.

Finding the overall sentiment of Robin Robert’s speech

robin_text %>% 
  unnest_tokens(word, X1) %>% 
  anti_join(stop_words) -> robin_words

# finding the sentiment of individual words
robin_words %>% 
  inner_join(get_sentiments("afinn")) -> robin_sentiment

Finding the highest sentiment words from Robin Robert’s speech

robin_sentiment %>% 
  arrange(desc(value)) %>% 
  count(word, value, sort = TRUE) %>% 
  filter(value %in% c("-5", "-4", "-3", "3", "4", "5")) %>% 
  ggplot(aes(word, value, fill=value)) + geom_col() -> plot_robin_sentiment

# Extra Visualization Formatting
plot_robin_sentiment +
  coord_flip() +
  ggtitle("Highest Sentiment Words from Robin Robert's ESPY Speech")+
  xlab('Word') +
  ylab("Value") + 
  theme_bw() -> plot_robin

Creating a wordcloud of the most prevalent words in Robin Robert’s speech

robin_words %>% 
  group_by(word) %>% 
  count() %>% 
  arrange(desc(n)) %>% 
  wordcloud2(demoFreq, size=1.6, color=rep_len( c("#16193B","#35478C",    "#4E7AC7", "#7FB2F0", "#ADD5F7"), nrow(demoFreq))) -> wordcloud_robin
word n
strength 7
sister 5
dear 4
research 4
sally 4
save 4
standing 4
courage 3
heard 3
honour 3

Analysis of the words from Robin Robert’s speech

Robin Robert’s speech was one of hope and extreme positivity. That showed in the highest sentiment words as they were all extremely positive. From words such as inspiring, grateful, and love the positivity that Roberts wanted to show shined through. As a lot of the speech was to bring awareness to bone marrow transplants the idea prevalence of the word “strength” and “research” add to that message.


Analysis of Michael Sam’s Speech at the ESPYs

Video of Michael Sam’s ESPYs Speech

Michael Sam is known as the first openly gay NFL player. Michael Sam was an award-winning college football defensive end earning the title of SEC Defensive player of the year. After coming out he was awarded the Arthur Ashe Courage Award for his openness and courage he showed when coming out. Most of his speech was very emotional about his life and his experience of always living a lie.

Finding the overall sentiment of Michael Sam’s speech

sam_text %>% 
  unnest_tokens(word, X1) %>% 
  anti_join(stop_words) -> sam_words

# finding the sentiment of individual words
sam_words %>% 
  inner_join(get_sentiments("afinn")) -> sam_sentiment

Finding the highest sentiment words from Michael Sam’s speech

sam_sentiment %>% 
  arrange(desc(value)) %>% 
  count(word, value, sort = TRUE) %>% 
  filter(value %in% c("-5", "-4", "-3", "3", "4", "5")) %>% 
  ggplot(aes(word, value, fill=value)) + geom_col() -> plot_sam_sentiment

# Extra Visualization Formatting
plot_sam_sentiment +
  coord_flip() +
  ggtitle("Highest Sentiment Words from Michael Sam's ESPY Speech")+
  xlab('Word') +
  ylab("Value") + 
  theme_bw() -> plot_sam

Creating a wordcloud of the most prevalent words in Michael Sam’s speech

sam_words %>% 
  group_by(word) %>% 
  count() %>% 
  arrange(desc(n)) %>% 
  wordcloud2(demoFreq, size=1.6, color=rep_len( c("#16193B","#35478C",    "#4E7AC7", "#7FB2F0", "#ADD5F7"), nrow(demoFreq))) -> wordcloud_sam
word n
arthur 3
ashe 3
football 3
lot 3
love 3
start 3
tonight 3
words 3
award 2
bigger 2

Analysis of the words from Michael Sam’s speech

Michael Sam’s speech like most of the ones analyzed so far is very positive in sentiment. Almost all of the highest sentiment words are positive and include loved, love, and brilliant. These words of love about the love he has received since coming out. When looking at the most prevalent words they are relatively equal but words such as “gay” and “live” stand out to me as influential words.


Analysis of Stuart Scott’s Speech at the ESPYs

Video of Stuart Scott’s ESPYs Speech

Stuart Scott was an American sportscaster known for his likability personality and fun catchphrases. In 2014 Scott received the Jimmy V Award perseverance award. Scott battled cancer from 2011-2014 with him ultimately succumbing to the disease 6 months after his ESPYs speech. Scott’s speech has a lot in common with Jimmy Valvano in that it was a motivational speech focused on how you can live your life to be a better person. Scott was a loved personality in the sporting world and his speech reached into the hearts of all sports fans.

Finding the overall sentiment of Stuart Scott’s speech

scott_text %>% 
  unnest_tokens(word, X1) %>% 
  anti_join(stop_words) -> scott_words

# finding the sentiment of the individual words
scott_words %>% 
  inner_join(get_sentiments("afinn")) -> scott_sentiment

Finding the highest sentiment words from Stuart Scott’s speech

scott_sentiment %>% 
  arrange(desc(value)) %>% 
  count(word, value, sort = TRUE) %>% 
  filter(value %in% c("-5", "-4", "-3", "3", "4", "5")) %>% 
  ggplot(aes(word, value, fill=value)) + geom_col() -> plot_scott_sentiment

# Extra Visulization Formatting
plot_scott_sentiment +
  coord_flip() +
  ggtitle("Highest Sentiment Words from Stuart Scott's ESPY Speech")+
  xlab('Word') +
  ylab("Value") + 
  theme_bw() -> plot_scott

Creating a wordcloud of the most prevalent words in Stuart Scott’s speech

scott_words %>% 
  group_by(word) %>% 
  count() %>% 
  arrange(desc(n)) %>% 
  wordcloud2(demoFreq, size=1.6, color=rep_len( c("#16193B","#35478C", "#4E7AC7", "#7FB2F0", "#ADD5F7"), nrow(demoFreq))) -> wordcloud_scott
word n
people 9
fight 8
live 5
gonna 4
listened 4
love 4
ago 3
body 3
cancer 3
dad 3

Analysis of the words from Stuart Scott’s speech

Stuart Scott’s speech was motivational but almost heartbreaking. While the overall sentiment was positive with high sentiment words such as “wonderful”, “love”, and “inspiring” there were some high sentiment negative words such as “losing” and “die.” These negative words are interesting because Scott talks about death and leaving his family. In the most prevalent words category “people” take the trophy, but “fight” is not too far behind. The word “people” makes sense as he talks about what people should do with their lives and the word “fight” is related to all of the time he says to never give up and to fight.


Analysis of Lebron James, Carmelo Anthony, Chris Paul, and Dwyane Wade’s Speech at the ESPYs

Video of Lebron James & Co’s ESPYs Speech

This speech is a little bit different from the rest of the speeches. This speech was not for an award but was a speech from four of the best NBA players in 2016. Lebron James, Dwayne Wade, Carmelo Anthony, and Chris Paul took the ESPY stage to talk about racial violence and social change. This speech came after high-profile shootings in Dallas, Orlando, and Baton Rogue. The tone of the speech was serious and had a major call to action for professional athletes and everyday citizens.

Finding the overall sentiment of Lebron James & Co’s speech

blm_text %>% 
  unnest_tokens(word, X1) %>% 
  anti_join(stop_words) -> blm_words

# finding the sentiment of the individual words
blm_words %>% 
  inner_join(get_sentiments("afinn")) -> blm_sentiment

Finding the highest sentiment words from Lebron James & Co’s speech

blm_sentiment %>% 
  arrange(desc(value)) %>% 
  count(word, value, sort = TRUE) %>% 
  filter(value %in% c("-5", "-4", "-3", "3", "4", "5")) %>% 
  ggplot(aes(word, value, fill=value)) + geom_col() -> plot_blm_sentiment

plot_blm_sentiment +
  coord_flip() +
  ggtitle("Highest Sentiment Words from Lebron and Co's ESPY Speech")+
  xlab('Word') +
  ylab("Value") + 
  theme_bw() -> plot_blm

Creating a wordcloud of the most prevalent words in Lebron James & Co’s speech

blm_words %>% 
  group_by(word) %>% 
  count() %>% 
  arrange(desc(n)) %>% 
  wordcloud2(demoFreq, size=1.6, color=rep_len( c("#16193B","#35478C", "#4E7AC7", "#7FB2F0", "#ADD5F7"), nrow(demoFreq))) -> wordcloud_blm
word n
stop 6
it’s 5
athletes 4
change 4
tonight 4
violence 4
brown 3
communities 3
stand 3
time 3

Analysis of the words from Lebron and Co’s speech

Unlike the rest of the speeches analyzed this speech has a different tone. Most of the high sentiment words are negative which include things such as anger, violence, and distrust. These are all related to the somber and serious tone the four men presented. The most prevalent word was “stop” as the men called for a stop to racism and police brutality. The other important word was “change” which ties directly to their call to action.


Creating a bar chart comparing the average sentiments of each speech

Finding the average sentiment of each speech

jimmy_sentiment %>% 
  summarise(average = mean(value))-> jimmy_sentiment_average

blm_sentiment %>% 
  summarise(average = mean(value))-> blm_sentiment_average

scott_sentiment %>% 
  summarise(average = mean(value))-> scott_sentiment_average

sam_sentiment %>% 
  summarise(average = mean(value))-> sam_sentiment_average

robin_sentiment %>% 
  summarise(average = mean(value))-> robin_sentiment_average

jenner_sentiment %>% 
  summarise(average = mean(value))-> jenner_sentiment_average

Creating columns in order to analyze the average sentiment

jimmy_sentiment_average %>%
  mutate(title = "Jimmy's Speech") -> jimmy

blm_sentiment_average %>%
  mutate(title = "Lebron & Co's Speech") -> blm

jenner_sentiment_average %>%
  mutate(title = "Jenner's Speech") -> jenner

robin_sentiment_average %>%
  mutate(title = "Robin's Speech") -> robin

sam_sentiment_average %>%
  mutate(title = "Sam's speech") -> sam

scott_sentiment_average %>%
  mutate(title = "Scott's speech") -> scott

Combining the sentiments into one column

jimmy %>% 
  full_join(blm) %>% 
  full_join(jenner) %>% 
  full_join(robin) %>% 
  full_join(sam) %>% 
  full_join(scott) -> sentiment_combined_averages

Creating the visualization of the average sentiments of each speech

sentiment_combined_averages %>% 
  ggplot(aes(title, average, fill=average)) + 
  geom_col() +
  coord_flip() +
  ggtitle("Average Sentiment of ESPY Sports Speeches") -> plot_sentiment_avg

plot_sentiment_avg +
  ylab('Average Sentiment') +
  xlab('') + 
  theme_bw() -> plot_sentiment_avg_completed

Analysis of average sentiments

Overall most of these speeches were ones of positivity, motivation, and hope. Only the NBA player’s call to action speech had an average negative sentiment which makes sense as it was a different type of speech. The most positive speech goes to Michael Sam’s speech followed closely by Robin Roberts.


Finding the Overall Most Common Words from the Speeches

jimmy_text %>% 
  full_join(jenner_text) %>% 
  full_join(blm_text) %>% 
  full_join(robin_text) %>% 
  full_join(sam_text) %>% 
  full_join(scott_text) -> combined_text

combined_text %>% 
  unnest_tokens(word, X1) %>% 
  anti_join(stop_words) -> combined_words

combined_words %>% 
  group_by(word) %>% 
  count() %>% 
  arrange(desc(n)) %>% 
  wordcloud2(demoFreq, size=1.6, color=rep_len( c("#16193B","#35478C",    "#4E7AC7", "#7FB2F0", "#ADD5F7"), nrow(demoFreq))) -> wordcloud_combined
word n
people 29
i’m 23
tonight 20
it’s 18
life 18
that’s 17
family 14
time 14
coach 13
day 13

Analysis of the overall most common words from the speeches

The most common word from all the speeches is “people.” This word is used by most of the speeches as they are talking to the audience and the world. In both Stuart Scott’s and Caitlyn Jenner’s speeches “people” was the most common word. Other popular words that relate well to the overall theme were “life”, “family”, and “time.”


Analyzing the Length of Each Speech

# counting the number of words in each speech
jimmy_text %>%
  unnest_tokens(word, X1) %>%
  count(word) -> jimmy_count

blm_text %>%
  unnest_tokens(word, X1) %>%
  count(word) -> blm_count

jenner_text %>%
  unnest_tokens(word, X1) %>%
  count(word) -> jenner_count

robin_text %>%
  unnest_tokens(word, X1) %>%
  count(word) -> robin_count

sam_text %>%
  unnest_tokens(word, X1) %>%
  count(word) -> sam_count

scott_text %>%
  unnest_tokens(word, X1) %>%
  count(word) -> scott_count

Creating a new column for the counted words

scott_count %>%
  mutate(title = "Scott's Speech") -> scott_count_title

jimmy_count %>%
  mutate(title = "Jimmy's Speech") -> jimmy_count_title

blm_count %>%
  mutate(title = "Lebron & Co's Speech") -> blm_count_title

jenner_count %>%
  mutate(title = "Jenner's Speech") -> jenner_count_title

robin_count %>%
  mutate(title = "Robin's Speech") -> robin_count_title

sam_count %>%
  mutate(title = "Sam's speech") -> sam_count_title

Combining together the counted words

scott_count_title %>% 
  full_join(blm_count_title) %>% 
  full_join(jenner_count_title) %>% 
  full_join(robin_count_title) %>% 
  full_join(sam_count_title) %>% 
  full_join(scott_count_title) %>% 
  full_join(jimmy_count_title) -> combined_count_title

Creating the visualization for the word count of the speeches

combined_count_title %>% 
  ggplot(aes(title, n, fill=title)) + 
  geom_col(show.legend = FALSE) +
  scale_fill_manual(values =     c("#16193B","#35478C","#4E7AC7","#7FB2F0","#ADD5F7", "#16193B")) +
  coord_flip() +
  ggtitle("Word Count of ESPY Sports Speeches") -> plot_count_title

plot_count_title +
  ylab('Word Count') +
  xlab('') + 
  theme_bw() -> plot_count_title_completed

Analysis of the length of each speech

While the length of each of these speeches varies because of time constraints, type of speech, and other factors it still creates an interesting conversation. Famously in Jimmy Valvano’s speech, he tells the teleprompter who flashed him a 30 seconds remaining message “I got tumors all over my body and I’m worried about some guy in the back going ‘30 seconds’.” His speech was the longest by almost 500 words. The shortest speech was Lebron and Co’s speech which is interesting because they had four speakers.


Looking for the Most Common Bigrams Between all the Speeches

Finding the bigrams for each speech

scott_text %>% 
  unnest_tokens(bigram, X1, token ="ngrams", n=2) -> scott_bigrams

blm_text %>% 
  unnest_tokens(bigram, X1, token ="ngrams", n=2) -> blm_bigrams

jimmy_text %>% 
  unnest_tokens(bigram, X1, token ="ngrams", n=2) -> jimmy_bigrams

robin_text %>% 
  unnest_tokens(bigram, X1, token ="ngrams", n=2) -> robin_bigrams

sam_text %>% 
  unnest_tokens(bigram, X1, token ="ngrams", n=2) -> sam_bigrams

jenner_text %>% 
  unnest_tokens(bigram, X1, token ="ngrams", n=2) ->jenner_bigrams

Joining all the bigrams together

jimmy_bigrams %>% 
  full_join(jenner_bigrams) %>% 
  full_join(blm_bigrams) %>% 
  full_join(robin_bigrams) %>% 
  full_join(sam_bigrams) %>% 
  full_join(scott_bigrams) -> combined_bigrams

Working with the bigrams to find the number of occurances

# separating word1 and word2 from each other
combined_bigrams_separated <- combined_bigrams %>%
  separate(bigram, c("word1", "word2"), sep = " ")

# removing stop_words from the bigrams
combined_bigrams_filtered <- combined_bigrams_separated %>%
  filter(!word1 %in% stop_words$word) %>%
  filter(!word2 %in% stop_words$word)

# counting the bigrams
combined_bigram_counts <- combined_bigrams_filtered %>% 
  count(word1, word2, sort = TRUE) 

# putting the bigrams back together and filtered for bigrams that occur more than 5 times
    # exluded "NA NA" because it was not applicable
combined_bigrams_filtered %>% 
  unite(combograms, c(word1, word2), sep = " ", remove=FALSE) %>% 
  filter(!combograms == "NA NA") %>% 
  count(combograms, sort = TRUE) %>% 
  filter(n>5) -> combined_combograms_count

Creating the visualization

combined_combograms_count %>% 
  ggplot(aes(combograms, n, fill=combograms)) +
  scale_fill_manual(values = c("#16193B","#35478C","#4E7AC7","#7FB2F0","#ADD5F7")) +
  geom_col(show.legend = FALSE) +
  ggtitle("Bigrams that occured more than 5 times") -> plot_combined_combograms

plot_combined_combograms +
  ylab('Word Count') +
  xlab('') + 
  theme_linedraw() -> plot_combograms

Analysis of the common bigrams

While the chart above looks almost empty it shows an interesting story. The chart is counting bigrams in all of the speeches that occur more than 5 times. To my surprise, there was only one bigram that fit the criteria and it was “Arthur Ashe.” Arthur Ashe was a professional tennis player who is the only black man to win the singles title at Wimbledon. He is also who the Arthur Ashe Courage Award is named after. While it is not surprising that his name shows up a lot throughout the text, interestingly, that mentions of the name are more than the award. As an example, Jimmy Valvano says “I can’t tell you what an honor it is to even be mentioned in the same breath with Arthur Ashe.” These mentions show an interesting narrative of mentioning who the award was named after in the speech. Stuart Scott mentions Jimmy Valvano in his speech while receiving the Jimmy V Perseverance award which supports this claim.


Conclusion

Overall in this project, I have come to a few conclusions. I have found many similarities between the different ESPY speeches but also noticed some major differences. When it comes to sentiment typically the speeches are very positive, but sometimes they can lead negative. Overall the most popular words of “people” and “life” relate to the ideas of motivation and perseverance. These speeches are all influential and help to explain the spirit of the ESPYs.