Campaigning on Twitter: The Effect of Player and Team Tweets on 2015 All-Star Game Voting
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Presented at Western Decisions Institute, April 2016; presented at SABR 46, July 2016; presented at Saberseminar, 2016
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Campaigning on Twitter: The Effect of Player and Team Tweets on 2015 All-Star Game Voting
- 1. CAMPAIGNING ONTWITTER:THE EFFECT OFVERIFIEDTWEETS ON THE 2015 MAJOR LEAGUE BASEBALLALL-STAR GAME VOTING Allison R. Levin Professor and Consultant @consultsnaps
- 2. Scope of Project ■ To determine whether social media, mainlyTwitter has a significant effect on who receives the most votes for the ASG ■ Prior Research 40 year study – 1980s – Advanced Stats – 1990s- Advanced andTraditional – 2000s- Popularity – 2010s-Twitter with Stats
- 3. Scope of Project ■ 2015 ASG – Ballot released on May 3 and voting closed on July 3 – Throughout voting MLB released the names of the top 1/3 of players receiving votes at each position ■ Collected data for each voting period
- 4. Variables ■ Dependent variable- Place on Ballot – 1st through 5th or off ■ IndependentVariables (all indexes) – Traditional Statistics – Advanced Statistics* – Player Popularity – Team Popularity* – TeamTweet Efficiency (ratio engagement to tweets) – PlayerTweet Efficiency
- 5. Model ■ Applied an Ordered Logit – Non-linear procedure that looks at which variables influence where the player finished in the voting at each ballot update ■ Odds of being in a higher place on the ballot rather than being lower
- 6. Significance is based on Standard Errors which are not reported on the table Variable Period 1 Period 2 Period 3 Period 4 Period 5 Period 6 Est. Odds Ratio Est. Odds Ratio Est. Odds Ratio Est. Odds Ratio Est. Odds Ratio Est. Odds Ratio RPPALL 0.015 1.015 0.014 1.014 0.014 1.014 0.014 1.014 0.013 1.013 0.014 1.015 TI((P1-P6) 0.002 1.002 -0.117 0.890 -0.102 0.903 -0.089 0.914 -0.080 0.923 -0.080 0.923 TTE(P1-P6) 0.120 1.128 0.566 1.762 0.041 1.042 0.161 1.175 0.190 1.209 0.133 1.142 PTE(P1-P6) 0.034 1.035 0.119 1.127 0.019 1.019 0.006 1.094 0.062 1.064 -0.010 0.990 R-SQUARE 0.2387 0.4267 0.3157 0.3381 0.3183 0.3050 Coefficients in RED are statistically significant at the .01 level; those in GREEN at the .05 level; those in BLUE at the 0.10 level. All tests are one-tailed. Cumulative Logit Results forVoting for Major League All-Stars, 2015
- 7. HowTeams are usingTwitter Focus on performance statistics Teams can useTwitter to respond to what fans are voting on in a particular period
- 8. HowTeams are usingTwitter Focus on player popularity
- 9. Players onTwitter ■ Less comfortable – Focus on fandom/parasocial relationship – Pump other players
- 10. TeamTweet Efficiency over whole ballot 0.0 100.0 200.0 300.0 400.0 500.0 600.0 700.0 800.0
- 11. Moving forward Age and relationships to voters 0 1 2 3 4 5 6 7 8 9 10 AuthorizedTwitter Accounts ByTeam Yes No
- 12. Moving Forward -Multinomial logic ■ Relax the ordered logit’s assumption that the parameter estimates do not vary by position on the ballot ■ Allows me to look at odds of being in 1st vs. 2nd, etc. ■ Should reduce correlation between fandom and tweets
- 13. Moving Forward- Nationality/Identification and Twitter Engagement 1 1 6 12 2 26 1 1 1 5 1 175 23 Aruba Brazil Canada Cuba Curacao Domican Republic Japan Netherlands Panama Puerto Rico South Korea United States Venezuela 0 20 40 60 80 100 120 140 160 180 200 Birth Country of Players on 2015 ASG Ballot
Editor's Notes
- Limitation- top 1/3- where they finished. Weak Twitter historical data available. Larger goal- understand how social media can be used by MLB, teams and players successfully Long term- understand WHY tweets are liked, RT, responded to
- Larger goal- understand how social media can be used by MLB, teams and players successfully Long term- understand WHY tweets are liked, RT, responded to Every tweet sent from team and players mentioning ASG
- Correlation between popularity so high- only used Player Pop Correlation between stats so high only used traditional
- In every period, a one-point increase in the player popularity index increases the odds of being in a higher place on the ballot rather than being lower in the voting by about 1.014 times The odds of being in a higher category are about 90% of the odds of being lower on the voting scale for a one-point increase in the index of traditional statistics Team tweet efficiency seems to make a statistically significant difference in the player’s place of the ballot in periods 2, 4, 5 and 6. Player tweet efficiency has a statistically significant effect on the outcomes in the ordered logit model estimated for periods 2, 4, and 5, with the largest effect found in Period 2 Point out vote reduction at end of 4- why both pop
- Miggy Jersey Boys- todd Fraizer
- Even when normalize still widely different Clear that there are diminishing returns Often same tweet- different results
- Average age all-star viewer mid-50s how many on twitter- how many vote AL- 9 NL-8 Twitter to be effective at getting votes as to generate engament and fandom-- clients of Jay-Z, friends of famous people, videos of celebrities, russell wilson
- Expect biggest impact to happen at 3-5—smaller markets
- Look at geotags