Under Pressure: A Case Study of the Effects of External Pressure on MLB Players using Twitter Sentiment Analysis

Under Pressure: A Case Study of the

Effects of External Pressure on MLB

Players using Twitter Sentiment Analysis

BY Jonathan Huntley

ADVISOR • Alicia Lamere

EDITORIAL REVIEWER • Ronald Deluga

_________________________________________________________________________________________

Submitted in partial fulfillment of the requirements for graduation

with honors in the Bryant University Honors Program

November 2020

Table of Contents

Abstract ..................................................................................................................................... 1

Introduction ............................................................................................................................... 2

Literature Review ...................................................................................................................... 2

Performance Under Pressure ................................................................................................. 2

Psychological Momentum ..................................................................................................... 4

Other Findings ....................................................................................................................... 5

This Investigation .................................................................................................................. 5

Research Questions ................................................................................................................... 5

Research Methodology.............................................................................................................. 6

Player Selection ..................................................................................................................... 6

Compiling Data ..................................................................................................................... 6

Sentiment Analysis ............................................................................................................... 7

Grouping Data ....................................................................................................................... 8

Data Analysis & Results ........................................................................................................... 8

Correlation Analysis.............................................................................................................. 8

2018 Season ........................................................................................................................ 10

Psychological Momentum and Pressure Study ................................................................... 11

Conclusions ............................................................................................................................. 16

Appendices .............................................................................................................................. 18

Appendix A – Price vs. Sale Comparison before and after Red Sox Debut ....................... 18

Appendix B – ANOVA Output ........................................................................................... 19

References ............................................................................................................................... 20

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ABSTRACT

Performance under pressure and psychological momentum are well-documented topics in

sports psychology, but most research focuses on “in-game” pressure. This study views

pressure more broadly to examine how the external pressure of fans, quantified using the

sentiment of tweets mentioning the players, can affect how MLB players perform.

Although external pressure is intangible, it can impact a player’s psyche and performance.

This investigation focuses on players Chris Sale and David Price. A new process was

developed leveraging the Vader package in Python that can generate tweet sentiment to

compare to several performance metrics from Baseball Reference.

Results proved to be promising with correlation analysis pointing to some association

between sentiment and performance. There was also an observed difference in how both

players handled the pressure depending on whether they played for a small or large market

team. An anecdotal study of the 2018 season showed even more interesting differences

between Sale’s and Price’s performance and Twitter sentiment. Price’s performance and

Twitter’s sentiment moved in a cyclical manner throughout the season whereas Sale’s results

were much more consistent and less sensitive to change. Finally, a study focused on the

impact of both pressure and past performance on future outings showed results consistent with

past studies on the subject. For example, Sale was most likely to perform well under pressure

if he preceded the start with a very good or bad outing rather than an average outing.

Information like this could be useful for front offices and managers.

More analysis should be conducted to confirm and expand on the findings of this project.

However, this case study can be used as a foundation for a new and innovative approach to

player evaluation, ultimately complementing existing methods and informing decisions

regarding otherwise intangible factors.

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INTRODUCTION

Serving one of the largest markets in the MLB, Boston Red Sox players are heavily

scrutinized by scores of media, talk-show hosts, Twitter trolls, and dedicated fans. Players

who come to Boston can be beloved (David Ortiz, Brock Holt, Chris Sale) or hated (Pablo

Sandoval, Carl Crawford, David Price) with equal intensity depending on their attitude and

performance here. Some players seem to thrive under the pressure where others crack. This

study aims to determine whether some players react differently to external pressure by

comparing performance metrics to the sentiment of fan tweets.

Professional baseball is a multimillion-dollar industry. Some players are given contracts of

300 to 400 million dollars. When there is this magnitude of money involved, it is important

that front offices make the right decision when it comes to choosing and supporting players.

David Price ($217M), Carl Crawford ($142M), and Pablo Sandoval ($95M) are three recent

Red Sox contracts that are widely viewed as mistakes despite the success all three players had

on other teams. Conversely, David Ortiz initially signed with the Red Sox for $1.25M after

being released and went on to have a Hall of Fame caliber career. If the Red Sox front office

was able to understand the relationship between external pressure and performance, perhaps

they could make better decisions initially or provide some sort of intervention to struggling

players with bloated contracts.

LITERATURE REVIEW

Yogi Berra said that “baseball is ninety percent mental and the other half is physical”. Since

the time he retired in 1965, the field of sports psychology has continuously advanced. It is

from this field that most of the literature for this project will be drawn. Some of the key

findings from past studies relate to how performance is affected by pressure and

psychological momentum.

Performance Under Pressure

It is generally accepted that a graph depicting pressure or physiological arousal on the x-axis

and performance on the y-axis would be a parabolic shape (Luiselli & Reed, 2011) (See

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Figure 1). With minimal pressure, performance would suffer due to indifference or disinterest.

As the amount of pressure increases, performance would improve until a maximum point is

reached where an athlete is “locked in” at the top of their game and in a state of mind called

“flow state”. Eventually, more pressure will lead to feelings of anxiety and nervousness that

will ultimately deteriorate performance (Williams, 2010).

Figure 1: There is a parabolic relationship between performance and stress (Bradberry, 2014).

Martin (2019) provides several reasons why nervousness might interfere with performance.

First, nervousness consumes energy that could be more efficiently utilized. Secondly, anxiety

narrows an athlete’s attention potentially preventing them from picking up on external cues.

Lastly, the rush of adrenaline that accompanies anxiety can destroy the timing of skilled

routines that are an important part of sports.

Masters et al. (1993) developed a “Reinvestment Scale” aimed at predicting who would

perform the worst under stress. They theorized that those who scored higher on the

reinvestment scale disrupted the “automaticity of skilled performance” by investing too much

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of their attention to their action when under pressure. This supports the idea that “thinking too

much” will hurt one’s performance. The scale included three elements that would indicate

whether a person is more susceptible to “deautomatization”. They include rehearsal (a

tendency to mentally rehearse emotional events), private self-consciousness (the amount of

attention one pays to their thought processes), and public self-consciousness (the amount that

a person is concerned with how others view them). The higher a person rates on any of these

factors, the more likely they will succumb to pressure.

Psychological Momentum

Several studies have concluded that past performance is a significant factor when predicting

whether a player will succeed under pressure. One study analyzed NFL games and found that

the probability of failure was highest when the pressure was high and the play was preceded

by a failure (Harris et al., 2019).

In a separate study that focused on individual baseball players completing simulated at-bats,

findings were similar, showing an interaction effect between past performance and pressure.

Interestingly, the study found that players on either a cold or hot streak were more likely to

succeed under pressure as compared to those who were not on a streak. Researchers attributed

this to the fact that in a cold streak, a player will shift their attitudinal focus inward and focus

more on fundamental skills than external pressure. Conversely, those on a hot streak will have

heightened self-confidence or perceived control that improves their external attitudinal focus

and makes them more immune to the pressure. Additionally, those who succeeded under

pressure performed much better after the pressure was removed (Gray et al., 2013).

Another relevant finding came from a study by Golding et al. (2017). Athletes and non-

athletes were asked to pitch both with a crowd offering positive and negative criticism.

Athletes were more affected by the criticism presumably because they care more about their

performance, but the type of reaction and the magnitude was highly dependent on the person.

This seems to suggest that some athletes are affected more by external pressure than others.

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Other Findings

One article discussed different ways to measure fan passion including the extent to which a

team occupies the fan’s heart, mind, body, and soul. Fan passion could be a measure to

determine how much pressure a player will be under and/or how likely they will be to interact

with fans on social media (Wakefield, 2016).

The idea of clutch, having an innate ability to perform in high leverage situations, is a

controversial one in baseball. There are many studies of clutch that have shown that given a

large enough sample, those who perform best in high pressure situations are those that

perform the best in low pressure. However, players like David Ortiz make it hard to believe

that clutch doesn’t exist, and many managers do believe that some players are more capable in

high leverage situations (Perrotto, 2018).

This Investigation

Most of the aforementioned literature focuses on short-term, in-game pressure while this

project hopes to expand the concept of pressure to include the continuous off-field pressure a

player faces when playing in large markets like Boston, New York, or Los Angeles.

Additionally, many of these articles were controlled experiments that did not include

professional baseball players. This investigation will use Twitter sentiment analysis as a proxy

for external pressure on professional baseball players and use actual game performance

metrics to determine the interaction between pressure and performance.

RESEARCH QUESTIONS

The fundamental questions that this study will seek to answer are the following:

1. Can external fan pressure be quantified using tweets and is this measure related to

player performance in a meaningful way?

2. Do some baseball players perform differently under high external pressure than

others?

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3. How can front offices predict how players will react to varying levels of external

pressure?

RESEARCH METHODOLOGY

Player Selection

To answer the research questions presented, a case study was completed comparing Chris

Sale and David Price. These players were selected because they are players who had similar

careers prior to joining the Red Sox (See Appendix A) except for one important difference:

their perceived reaction to external pressure. Both are left-handed pitchers who joined the

Boston Red Sox between 2016 and 2017 after playing in smaller markets. They are within 3

years of age and before joining the Red Sox had very comparable statistics (David Price Stats,

n.d.) (Chris Sale Stats, n.d.).

When the two came to Boston, however, their first few seasons were very different (See

Appendix A). In general, David Price did not perform as expected and fans were very critical

of him. In contrast, Sale began dominating and fans embraced him with open arms. It is

possible that the difference stemmed from their reaction to the increased external pressure.

Furthermore, this could be related to their social media habits. The largest differentiation

between Price and Sale when they joined the Red Sox was that Price was an avid Twitter user

who had publicized Twitter battles with others, including an Umpire while in Tampa Bay

(Rymer, 2013). Sale, on the other hand, did not have any social media accounts and appeared

to block out the noise (It Doesn’t Sound Like Chris Sale Will Be On Twitter, Social Media

Any Time Soon, 2016).

Compiling Data

The methodology for this case study included using tweets mentioning Sale and Price as a

proxy for external pressure from fans. Using the Python package, GetOldTweets3, a process

was developed for compiling tweet text, retweets, favorites, dates, and more with a user

defined time-period and query (Mottl, 2018/2020). All tweets mentioning “David Price” and

“Chris Sale” were collected from the time of their starting pitching debut (2010 and 2012,

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respectively) until the end of 2019. Word Clouds were created from the resulting text using

the wordcloud package and the data was cleaned to remove advertisements and other

irrelevant tweets (See Figure 2) (Mueller, 2020).

Figure 2: Word Clouds produced from tweets mentioning David Price and Chris Sale

Additionally, performance data from the same dates were collected from Baseball Reference

game logs (MLB Stats, Scores, History, & Records, n.d.). The date, innings pitched (IP),

earned run average (ERA), win probability added (WPA), and game score (GSc) from each

pitcher’s outings were compiled. WPA is a measure of how much a player’s performance

increased their team’s chance of winning. A higher WPA indicated better performance

especially in high leverage situations (Slowinski, 2010). GSc is a metric developed by Bill

James to quantify a starting pitcher’s overall performance. A GSc of 50 to 60 is considered

about average whereas 80 plus is considered very impressive (What Is a Game Score?, 2020).

Sentiment Analysis

To determine the sentiment of the tweet text, the Vader Python package was employed

(Hutto, 2020). This package is intended for sentiment analysis of social media posts. The

package would break down tweets into individual words and categorize them as positive,

negative, or neutral. Based on this categorization, a weighted average compound score was

determined on a scale from -1 being most negative to 1 being most positive. This compound

score is referred to interchangeably with sentiment during this study.

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Grouping Data

Once the data was compiled, it was then grouped for analysis. Twitter sentiment data was

grouped by date and again by the time periods between starts for Price and Sale. Sentiment

and performance metrics were merged into one final data set that included various metrics

about a particular start and the Twitter activity leading up to that start. A visualization of the

data flow described can be found in Figure 3.

Figure 3: This figure shows the transformation of the source data from Twitter and Baseball Reference into a final data set

for analysis.

DATA ANALYSIS & RESULTS

Correlation Analysis

To confirm that sentiment relates to a player’s performance, correlations between

performance and sentiment following each game were examined. Interestingly, after joining

the Red Sox, the correlations between Price’s performance and Twitter’s reaction increased

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substantially--especially when the performance included high leverage situations. As seen in

Figure 4, the correlation between WPA and subsequent sentiment increased from .21 when

playing for small market teams to .43 on the Red Sox. Conversely, the same statistic

decreased from .43 to .31 for Chris Sale. Expressed differently, compared to Price’s seasons

with small market teams and Sale’s seasons with the Red Sox, Price experienced higher

degrees of Twitter responses for good or bad performances. This could translate to more

pressure being placed on his performance in subsequent outings. It is also evidence that

Twitter sentiment is associated with performance and could be a suitable surrogate for

external pressure.

Figure 4: Correlation between WPA and Sentiment for Price and Sale before and after joining the Red Sox. While in Boston,

Price experienced higher degrees of Twitter responses for good and bad performances which could lead to more pressure in

future outings.

Sentiment preceding games and subsequent performance were then examined. The results

show that there is a relationship between the two. Both pitchers exhibited some correlation

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between the percent of tweets with negative sentiment leading up to a game and ERA of that

game (.30 and .47 respectively).

This brief analysis shows that there may be some association between tweet sentiment and

various performance metrics. Additionally, there is some proof that this association could be

different depending on the player and whether they play for a small or large market team.

2018 Season

The next component of exploratory analysis included a detailed study of the 2018 season.

Figures 5 and 6 show average daily tweet sentiment and Game Scores for every start during

the 2018 season that ended in a Red Sox World Series title. This study was consistent with the

findings of the correlation analysis that showed higher degrees of Twitter response for David

Price than for Chris Sale.

Figure 5: David Price 2018 Average Sentiment and Performance. Price’s worst performances (usually against the Yankees)

were often preceded by decreasing sentiment, potentially a signal of increased pressure.

Price’s season appears cyclical with prolonged periods of overall increasing or decreasing

sentiment that appears roughly consistent with his performance. The graph shows that Price’s

worst performances (usually against the Yankees) were preceded by decreasing sentiment,

potentially a signal of increased pressure.

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Figure 6: Chris Sale 2018 Average Sentiment and Performance. Sale’s 2018 season was much less cyclical in sentiment and

performance than Price’s with brief spikes for extraordinary events.

Conversely, sentiment of tweets mentioning Sale held relatively constant over time and did

not display the same cyclical nature as Price. Both performance and sentiment were relatively

stable outside of brief spikes for extraordinary events such as a poor outing against the

Braves, a shutout against the Yankees, and a stomach illness during the ALCS. This could

potentially be related to the differing social media habits of the two players. Price is very

active on Twitter so it makes sense that his performance and Twitter sentiment could be

linked whereas Sale does not even have an account which could explain the lack of Twitter

sensitivity.

Overall, this element of the case study showed compelling evidence that Twitter sentiment

and performance are somehow related and that the relationship can be different depending on

the player.

Psychological Momentum and Pressure Study

The final element of this case study aimed to test the results of three studies mentioned in the

literature review above on psychological momentum and pressure. Psychological momentum

can be defined as a person’s propensity to perform better or worse based on their most recent

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performance. Several studies have concluded that past performance and pressure have some

sort of interaction effect that can impact future performance.

There were three main conclusions from the literature review that were examined in the

context of the David Price and Chris Sale study:

1. Harris et al. concluded that the probability of poor performance is highest when the

pressure is high and there was a preceding poor performance (2019).

2. Gray et al. concluded that players on either a hot or cold streak were more likely to

perform well under pressure than an average player (2013).

3. Golding et al. concluded that athletes are more likely to be affected by criticism, but

the type and magnitude of their reaction is highly dependent on the person (2017).

To examine these conclusions, each outing for David Price and Chris Sale was flagged as a

Good, Bad, or Average performance based on Game Score. Game Score was used as a metric

because it does the best job of quantifying a starting pitcher’s overall performance. In

Addition, Twitter sentiment leading up to a start was classified as largely positive, largely

negative, or neutral. Nine groups of starts were then compiled based on the combination of

previous performance and Twitter sentiment. Figure 7 shows box plots for both pitchers’

performance.

Additionally, a single factor Analysis of Variance (ANOVA) hypothesis test was conducted to

determine whether the means of any the groups were significantly different from one another.

The ANOVA output for both pitchers is located in Appendix B. The p-values for both tests

are below .20 indicating that there is an 80% chance that there are differences in mean game

score among the nine groups referenced above and displayed in the box plots of Figure 7.

Figure 8 shows ANOVA plots for both pitchers for further analysis.

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Figure 7: David Price and Chris Sale Performance after various levels of pressure and past performance.

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Figure 8: Analysis of Variance Plots for David Price and Chris Sale that show the mean GSc of starts after various

combinations of prior performance and Twitter sentiment. Consistent with the literature review, both pitchers perform best

under neutral sentiment, Chris Sale performs best under the pressure of negative sentiment when entering the game on a

“cold streak” or “hot streak”, and Price generally performs best after poor starts highlighting the differences between his

and Sale’s reactions to pressure and psychological momentum.

35.00

40.00

45.00

50.00

55.00

60.00

65.00

70.00

Positive Neutral Negative

Game Score

Twitter Sentiment Preceding Game

Chris Sale ANOVA Plot

Good GSc Avg GSc Bad GSc

Positive Neutral Negative

Game Score

Twitter Sentiment Preceding Game

David Price ANOVA Plot

Good GSc Avg GSc Bad GSc

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The above ANOVA plots display the mean game score for each group that contains outings

flagged as following positive, negative, or neutral sentiment and good, bad, or average

previous performance. Twitter sentiment is meant to be a proxy for pressure and previous

performance indicates whether the player is entering the game on a “cold streak”, “hot

streak”, or neither. Viewing these plots, one can not only learn about the individual

relationships between future performance and past performance or pressure, but one can also

visualize the interaction effect that could be occurring between the two independent variables.

This analysis yielded several key insights that were consistent with the literature review. First,

as seen in Figure 8, both pitchers had their highest mean game scores after a period of neutral

sentiment. In other words, the pitchers performed better when there was limited pressure from

fans. Additionally, performance was generally the worst when there was negative sentiment

from fans. This is consistent with the conclusion of Harris et al. that higher pressure was

associated with worse performance.

Secondly, analysis of Chris Sale shows remarkable consistency with the findings of Gray et

al. When viewing Sale’s ANOVA plot in Figure 8, the spread between average previous GSc,

and good or bad previous GSc increases substantially under negative sentiment. This indicates

that when Sale experiences large amounts of external pressure, quantified by large volumes of

negative tweets, he performs better when on a “cold streak” or a “hot streak” compared to

starts following an average performance. This phenomenon is exactly what Gray et al

reported.

Finally, viewing David Price’s results supports the conclusions of Golding et al. Although

there are some similarities between trends in Price’s data and that of Sale, such as both

pitchers performing relatively well after neutral sentiment, overall, it appears Price reacts

quite differently than Sale does in similar conditions. For example, Price’s highest average

game scores were following bad performances. Furthermore, Price’s worst average game

score is following a good performance and period of negative sentiment. These findings

support the idea that players react differently to pressure and psychological momentum. It is

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easy to see how this concept could be extended to front offices so that they can find players

who will react in desirable ways depending on the pressure associated with their team.

CONCLUSIONS

Overall, this case study serves as a compelling proof of concept for future research. It is

impossible to draw far-reaching conclusions from a study with only two players; however, the

findings presented can be used as building blocks for an entirely new area of player

evaluation. Too often teams sign free agents or trade for players with impressive numbers

without determining whether the player would be a good fit for their organization and their

market. Twitter sentiment analysis could be a tool to compliment traditional methods and give

front offices an edge.

The first research question presented in this analysis was whether external fan pressure could

be quantified using tweets. It was further questioned if this proxy for pressure would have any

relationship with player performance. Based on this study, the answer to both questions is yes.

The new process for collecting, cleaning, and analyzing tweets mentioning players was

successful and could be scalable to collect information on any modern player. Furthermore,

correlation results were promising showing at least some association between pressure and

performance for both Chris Sale and David Price and differences in that correlation depending

on the player and the team they played for. Furthermore, ANOVA tests that included

sentiment and psychological momentum produced results similar to what would be predicted

by other scholars in the field. Finally, anecdotal information from the 2018 season showed

compelling evidence that there could be a meaningful relationship between Twitter and player

performance.

The second research question asked whether some players would perform differently under

high external pressure than others. Again, this answer is yes based on the comparison of

David Price and Chris Sale. The two lefty pitchers had strikingly similar resumes before

joining the Red Sox, where external pressure undoubtedly increased. It is not difficult to

imagine that their reaction to this external pressure could be the source of subsequent

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differences in performance. Correlation analysis revealed that the association between tweet

sentiment and metrics like WPA could be different depending on the player and whether they

play for a big or small market team. For example, Price experienced higher degrees of Twitter

responses based on his play with the Red Sox, which could have led to more pressure and

worse performance in subsequent outings. Additionally, analysis of the 2018 season revealed

that Price’s performance and tweet sentiment was much more cyclical than that of Sale.

Lastly, Sale performed in manners that were consistent with the findings of scholars

mentioned in the literature review in the context of performance under pressure and

psychological momentum, whereas Price deviated from this trend, potentially due to his

fundamentally different reactions to pressure.

The final research question mentioned in this exploration related to how front offices could

predict how players will react to various levels of external pressure. This question will

unfortunately remain largely unanswered until a broader analysis is conducted expanding this

study’s initial findings. One cannot assume that the results of Chris Sale and David Price will

apply to every Major League ballplayer. Future studies should include expansions to other

players, teams, and positions.

Using data such as that which is presented here, front offices could profile players based on

their performance under pressure. Large market teams could find players that will thrive

under increased pressure and avoid players that will crumble. Conversely small market teams

can find value in players that failed in the bright lights of New York or Boston not due to lack

of talent, but due to their psychology. This study proves there is value in considering Twitter

data when making baseball decisions, but it is not until more information is compiled and

analyzed that macrotrends will be revealed allowing front offices to improve their decision

making. Eventually, this study can be the basis of a movement towards front offices adding

new players to their team who are better fits and providing improved support to current

players throughout the season.

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APPENDICES

Appendix A – Price vs. Sale Comparison before and after Red Sox Debut

Price vs. Sale Comparison at time of Red Sox Debut

David Price

Chris Sale

Position

Starting Pitcher

Throws

Left

Red Sox Debut

2016

2017

Age at Debut

Previous Teams

TBR, DET, TOR

CHW

Career ERA at Debut

3.09

3.00

Career FIP at Debut

3.20

3.06

Career WHIP at Debut

1.132

1.065

Career WAR at Debut

29.3

30.1

Cy Young Awards

Top 10 in Cy Young Voting

Top 25 MVP Voting

Previous All-Star Appearances

Acquired by Red Sox Through

Free Agency

Trade

Annual Salary Year 1 with Red Sox

$30M

$12M

Price vs. Sale Comparison after Joining Red Sox

David Price

Chris Sale

Position

Starting Pitcher

Throws

Left

Length of Tenure

4 seasons

3 seasons (and

counting)

2020 Season

Traded to Dodgers and

opted out of season

Tommy John Surgery

ERA

3.84

3.08

FIP

3.74

2.57

WHIP

1.204

0.970

WAR

10.4

15.2

Cy Young Awards

Top 10 in Cy Young Voting

Top 25 MVP Voting

All Star Appearances

2020 Expected Salary

$32M (50% LA, 50% BOS)

$30M

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Appendix B – ANOVA Output

David Price ANOVA: Single Factor

SUMMARY

Groups

Count

Sum

Average

Variance

GoodGSc, Pos

1689

54.484

347.458

GoodGSc, Neutral

3244

58.982

229.537

GoodGSc, Neg

1028

51.400

538.358

AvgGSc, Pos

2740

59.565

258.296

AvgGSc, Neutral

2342

58.550

247.741

AvgGSc, Neg

1754

53.152

316.070

BadGSc, Pos

652

54.333

256.970

BadGSc, Neutral

1548

64.500

170.783

BadGSc, Neg

1599

57.107

237.877

ANOVA

Source of Variation

P-value

F crit

Between Groups

3,310.33

413.792

1.493

0.160

1.692

Within Groups

77,624.32

280

277.230

Total

80,934.65

288

Chris Sale ANOVA: Single Factor

SUMMARY

Groups

Count

Sum

Average

Variance

GoodGSc, Pos

2168

61.943

263.997

GoodGSc, Neutral

3094

65.830

248.101

GoodGSc, Neg

1624

62.462

278.978

AvgGSc, Pos

671

61.000

484.400

AvgGSc, Neutral

2353

60.333

228.386

AvgGSc, Neg

1239

51.625

544.418

BadGSc, Pos

379

63.167

105.367

BadGSc, Neutral

767

63.917

264.992

BadGSc, Neg

1633

60.481

371.259

ANOVA

Source of Variation

P-value

F crit

Between Groups

3,418.33

427.291

1.401

0.197

1.699

Within Groups

66,501.77

218

305.054

Total

69920.09692

226

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