The Most Hated (And Loved) NBA Players – According To more than 8.5 Million Tweets

Prior to the tip-off of the 2022-2023 NBA season, Action Network decided to take a thorough look at the NBA fans’ Twitter activity. We wanted to find out what the vibe is  around the athletes on one of the biggest stages, the National Basketball Association. It’s pretty well-known that some players can face abuse online. To that end, we scoured Twitter to determine the most hated NBA players online.

We scraped and analyzed more than eight and a half million English tweets directed towards the top 50 NBA players from the 2021-22 season. Our data collection spanned the beginning of the 2021-22 season through and until September 23, 2022.

We then conducted a sentiment analysis on each of the scraped Tweets. This allowed us to conclude which players were targeted the most by negative Tweets. Conversely, we were also able to determine which players received the most ‘love’ from the fans as well. Action found that of the 8.5 million reviewed Tweets, 36% were deemed to have a positive sentiment, 46% were neutral, and only 18% were negative or insulting towards a player.

Most Hated NBA Player Analysis Key Findings:

• Marcus Smart received the highest share of negative Tweets. 41.03% of all Tweets directed towards him were deemed negative. 
• LeBron James was Tweeted at the most. He had more than 704k Tweets mentioning him, with the highest number of negative messages (115,764).
• LeBron was also the target of most positive Tweets. He earned 249,086 positive Tweets throughout this time frame. 
• Donovan Mitchell is the ‘least’ abused player in the top 50 in terms of the share of negative Tweets directed at him. He 'only' had 24.32% negative sentiment rate.
• LeBron James, Ja Morant and Ben Simmons received a combined 326,160 negative Tweets. 21% of all negative Tweets reviewed were directed at these three players.  

Players With the Highest Percentage of Negative Tweets

This list of players contains the players with the highest percentage of Tweets which are deemed as negative:

1. Marcus Smart – 41.03%
2. Draymond Green – 38.27%
3. Bam Adebayo – 37.33%
4. Trae Young – 37.24%
5. Jimmy Butler – 36.7%
6. Ben Simmons – 36.63%
7. Chris Paul – 36.46%
8. Jaylen Brown – 35.35%
9. Rudy Gobert – 34.66%
10. Jayson Tatum – 34.63%

Players With the Highest Total Number of Negative Tweets

Looking at the list of players, the following table will display who received the greatest number of abusive Tweets:

1. Lebron James – 116,764
2. Ja Morant – 107,056
3. Ben Simmons – 102,340
4. Trae Young – 83,717
5. Kevin Durant – 80,545
6. Kyrie Irving – 72,710
7. James Harden – 72,290
8. Stephen Curry – 57,025
9. Chris Paul – 55,634
10. Jimmy Butler – 55,165

Players With the Lowest Negative Sentiment

The following table shows the players with the lowest percentage of negative Tweets directed at them. Considering the high amount of Tweets targeting Steph Curry, it might come as surprise that he’s on this list, but the sheer percentage of the negative Tweets is low.

1. Donovan Mitchell  – 24.32%
2. CJ McCollum – 26.27%
3. Giannis Antetokounmpo – 26.65%
4. Kawhi Leonard – 26.77%
5. Nikola Jokic – 27.19%
6. Darius Garland – 27.41%
7. Stephen Curry – 27.45%
8. Domantis Sabonis – 28.03%
9. Zion Williamson – 28.30%
10. Bradley Beal – 28.33%

Fan sentiment online can be a tricky matter. One minute they can love a player and hate them in the next. Let's see which players can turn the tide on their Twitter sentiment in the coming 2022-23 season.

Be sure to following along with the Action Network for NBA odds, news, and updates as the season gets underway. NBA bettors can take advantage of Action's FanDuel promotion to get a free bet up to $1,000 if their first wager loses. FanDuel offers the best average NBA odds prices, so be sure to check in each game for the best lines!

Methodology

We used an API (Application Programming Interface) to scrape 10 Million tweets related to top NBA Players. We extracted only English tweets (8,795,826 tweets) and cleaned the data using Python programming language. The data cleansing consisted of the process of removing URLs, Hashtags, Mentions, Punctuation, Duplicates, Null Values, and special characters from tweets. Once the data was cleaned we used NLP (Natural Language Processing) techniques, TextBlob Python Library to be more specific, to analyze the sentiments of the data.

TextBlob returns the polarity of a sentence. Polarity lies between [-1,1], where -1 defines a negative sentiment, and +1 defines a positive sentiment. Once we had all cleaned tweets labelled as positive or negative, we grouped the data by date and player and calculated the percentage of negative and positive tweets for each date and each player.

The players on this list represent the top 50 players in the NBA according to CBS writers. 

The full dataset can be found here.