BLM x NBA: Social justice messages on jerseys - but are players really free to express themselves?

TLDR: In the aftermath of the killing of George Floyd and the movement for racial equality that has since received, deservedly so, additional attention, the NBA took a unique approach to social justice. The NBA has since allowed its players additional freedom of expression by means of social justice message featured prominently on their jersey where the athlete's surname would otherwise appear. 

Yet some players were less than enthused when they were told to choose from one of 29 pre-approved messages, citing the list being too restrictive or too restrained. 

Moreover, I find that comparing the share of messages that some may find are more 'politically-charged' on a team is correlated with the political leanings of the geography the team represents, begging the question,

Are the players really free to express themselves?

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I would first like to remind everyone, the views here are my own and not necessarily that of my employer. This can be a bit of a touchy subject, and although I do not believe this is the correct forum to discuss large complex problems such as racism or police reform, I welcome constructive comments and criticisms on the analytical approach I have taken here.

I first became interested in this NBA initiative when I noticed several players were upset by the NBA's pre-approved social justice messages limiting their individualism and ability to use a perhaps more-provocative message for social justice. Certainly, detractors such as FOX News (among others far less friendly) also complained that the so-called 'right-winged' messages were not available on the list. 

Regarding the NBA's list, many social justice messages appear very neutral: 'vote,' 'peace,' 'equality,' 'freedom,' among others are concepts that neither liberals nor conservatives could imply mean more to the other's camp than are simply universally accepted. Among the remaining messages, such as 'black lives matter,' or 'I can't breathe,' have - for one reason or another - become sensationalised and incendiary to some.

Nevertheless, the NBA re-started in its Orlando 'bubble' and its players took the court with their social justice messages across their shoulders. And although each player is also an individual, it is important to remember that each player is an employee of a team that represents a city (and often a unique state) which has its own identity. While the city does not employ the player directly, the team ownership is certainly required to appease its local fan base. Therefore, one might expect a team from a Republican-leaning area to avoid the 'incendiary' social justice messages - such as the Dallas Mavericks (from the state who has voted for the Republican presidential candidate since 1980) and the entire team's 'choice' to feature 'equality' on their jerseys.

Similarly, if I plot the team's share of these BLM messages and the county's 2016 Trump vote share, the results are fairly revealing (for obvious reasons, I exclude the Toronto Raptors from this analysis).

There are some exceptions to the rule. For example, the top two teams - the Portland Trail Blazers and Miami Heat - have 48% of their players with BLM-style messages on their jerseys, yet just 34% of Miami-Dade County voted for Trump in 2016. However, a somewhat-recent poll says only 27% of Miamians would vote for Trump in the upcoming election making it appear more similar to that of Multnomah County, Oregon (home of the Trail Blazers).

I leave all further inferences up to the reader. Please take what you will from the graph or the concept in general.

Keep your options open: Change in MLB viewership when substitutes are available

TLDR: Every television program on the air compete for the same sets of eyes. To understand the preferences of consumers, we can use television data to observe the changes in viewership when multiple programs air simultaneously.

Results suggest that 10% of would-be MLB viewers instead watch their local NBA team in a playoff game and only 3.6% watch the local NHL playoff game. In cities with two MLB teams and a NHL team, the MLB team A steals away more fans from MLB team B than the local NHL team playing playoff hockey!

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Every television program on the air - and increasingly, television programs on demand - compete for the same sets of eyes (my beautiful optometrist girlfriend assures me eyes usually come in sets).

Sporting events are no exception: looking way back to my second ever blog post, I discussed how at least some of the decline in the National Football League's Sunday Night Football broadcasts was due to the timing of the games and other programs like the Cubs-Cleveland World Series game 5 or the 2nd Presidential Debate.

To understand the preferences of consumers, we can use Nielsen television data to observe the changes in viewership when multiple programs air simultaneously. Nielsen is the media company that provides estimates on the number of people who tune in to a given television program.

For this calculation, I focus on Major League Baseball for two reasons: a) the data is readily available, and; b) there are plenty of games with and without overlap from similar television programs. I define a similar television program as a broadcast of other local teams of the four major North American sports leagues (where local is roughly teams in the same city or marketing area). Narrowing the definition to local teams ensures that we capture viewers who actually have a choice between the several options. Therefore, a the local MLB regular-season game can potentially overlap with:

• the local National Football League team's pre-season game;
• the local National Basketball League team's playoff game;
• the local National Hockey League team's playoff game, or;
• the other local Major League Baseball team's regular-season game in the same marketing area, with a focus on when they are not playing each other.
• e.g. New York, New York (not Sinatra but the Mets and Yankees).

I run an estimation to predict the total number of viewers for each MLB game, adding controls for the team, opponent, day of the week, the quality of the teams, and, finally, the availability of alternative sporting events. If the number of viewers declines in response to the availability of one of the aforementioned alternative sporting events, we would say the event and MLB are substitutes for each other. If we assume the 'cost' of watching a given sporting event stays relatively constant over time, the magnitude of the decline becomes an indicator of how strong of a substitute each event is for MLB.

Below is a graphical depiction of the results:

As expected, the number of viewers of the local MLB declines when an alternative sporting program is airing simultaneously. While it is no secret that on a per-game basis NFL is the most watched sport in North America, it is still quite astonishing that 30% of would-be MLB viewers turn off their local baseball team in favour of their local NFL team participating in a pre-season game! Some may argue that NFL pre-season overlaps the MLB regular-season at a time when the post-season fate of many MLB teams has already been decided and there is very little excitement in watching a losing MLB team finish out a season. But the estimation described above does in fact control for the quality of the MLB team meaning that on average, whether the team is leading the division or a perennial basement dweller, 30% of their fans rather watch a 'meaningless' pre-season NFL game.

Furthermore, 10% of would-be MLB viewers instead watch their local NBA team in a playoff game and only 3.6% watch the local NHL playoff game: in cities with two MLB teams and a NHL team, MLB team A steals away more fans from MLB team B than the NHL team playing intense playoff hockey takes from MLB team B!

While I would not necessarily prefer to watch the local NBA playoff game over the local NHL game (and likely never watch a pre-season NFL game), what do you guys think? Do the results seem reasonable? If all were playing at the same time, which would you choose to watch?

Let me know in the comments!

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Time out! Do some scores look worse than others?

TLDR: The outcome of (most) sports is determined not by the actual value of an individual's or team's performance, but by the relative performance.

Despite the fact that winners and losers are determined by the point differential, every sport summarises the score as the cumulative total points for each opponent. Athletes, coaches, fans, and pundits alike are then forced to calculate the point differential themselves in order to determine how close the game really is.

In the NBA, each team may call a time-out in attempt to alter the momentum of the game, particularly when the momentum is not in their favour or when their team is not in the lead. In the small window of time available to call for a time-out, if a team miscalculated how many points their team was trailing by, would they be more likely to call a time-out?

I find that teams are more likely to call a time out when they are down by a score spanning two tens places over a score spanning one tens place, even when the point differential is the exact same! For example, if a team is down 81-69, they are 10% more likely to call a time out than if they were down 83-71!

The outcome of (most) sports is determined not by the actual value of an individual's or team's performance, but by the relative performance. It matters not how many points (or goals, etc.) you have, but how many points you have in comparison to your opponent.

Despite the fact that winners and losers are determined by the point differential, every non-racing sport summarises the score as the cumulative total points for each opponent (please comment below if you have a counter-example). Athletes, coaches, fans, and pundits alike are then forced to calculate the point differential themselves in order to determine how close the game really is. Due to cognitive biases in the way we perceive numbers, we may then expect to see some interesting patterns in data pertaining to these cumulative vs relative scores.

The National Basketball Association (NBA) is a league where the average combined score of a single game is over 200 points and the average margin of victory is just 10 points. Each game can see multiple lead changes and notable swings in momentum between the two teams. During the course of the game, each team may call a time-out in attempt to alter the momentum of the game, particularly when the momentum is not in their favour or when their team is not in the lead.

But do certain game conditions entice teams to systematically choose when to call a time-out? Does trailing your opponent by certain values induce more time-outs than others? And what sort of mental arithmetic biases might we see when dealing with basketball scores?

To answer all of the above, I first began by collecting all of the play-by-play data for the 2016/2017 NBA regular season, conveniently provided by Reddit. I organise the data to the point where each observation is a single possession (generally, a possession begins every time the shot-clock is reset to 24 seconds). I collected data on the team, the opponent, the amount of time remaining in regulation, the quarter, and the current score. It is from the latter that I calculate the point differential.

Using a computer to calculate point differential, I am all but guaranteed to get the correct answer. However, using mental math, I am likely to make some mistakes. In fact, I first noticed that when the two scores span a difference of two tens places, e.g. 81-69, I was more likely to overestimate the difference: I erred by incorrectly thinking the losing team was further behind than 12 points. Yet, I often was able to accurately calculate the point differential if the scores span only one tens place, e.g. 83-71. In the small window of time available to call for a time-out, if one miscalculated how many points their team was trailing by, would they be more likely to call a time-out?

To test if others shared in my blunders, I ran a logit model to predict how many times a time-out is called based on the actual point differential and the tens-place point differential. Here I considered only the instances where the team calling the time out was a) trailing their opponent in score, and; b) the actual point differential was between 11 and 19 points. The latter limits the data to possessions where the team making the time-out decision is down by only scores spanning one or two tens places. I also control for the time remaining in the quarter and game, the team making the time-out call, and the score change since the last time-out/end of last quarter (also known as the 'run').

I find that teams are more likely to call a time out when they are down by a score spanning two tens places over a score spanning one tens place, even when the point differential is the exact same! Below is the graphical depiction this increase in probability of calling a time-out from the score spanning two tens places:

When down between 11 and 19 points teams are 10% more likely to call a time-out if the point differential spans two tens places than they are to call a time-out if the point differential only spans one tens place. Using the same example from above, if a team is down 81-69, they are 10% more likely (taking the overall number) to call a time out than if they were down 83-71!

I have shown there exists some evidence that certain scores induce more time-outs than others, namely when the point differential spans two tens places. I suggest that may be due to our tendency to systematically miscalculate the difference between two numbers which overstates the difference in certain circumstances. We may also see a similar effect when people are trying to calculate the time between two events, such as a layover at an airport: 7:35 to 10:10 is the same span of time as 5:00 to 7:35 despite that the former may look like a larger interval.

In the end, the same point differential and just a subtle change in the actual the scores can make all the difference in the world in how we perceive the margin of victory.

The impact of "Did Not Play - Rest" on the NBA

TLDR: "... [G]enerally, NBA games in which a player does not play due to rest result in a 5% decrease in viewership."

"However, considering the size and confidence of the effect that resting players have on the ratings of NBA games, it may be a little early to introduce new rules to address for this potentially overstated issue."


The issue of National Basketball Association (NBA) teams choosing to have individual players sit out regular season games for "rest" is highly contested. I do not want to waste time reiterating much of the same arguments and opinions of others. Instead I want to offer some insight into the magnitude of the effect on demand when NBA players rest and suggest that the issue of resting may be overstated,

If having players rest were to have an effect on the demand for basketball, I would argue it would be most evident in the broadcast ratings. Recall that attendance is traditionally the number of tickets sold: if the announcement of players resting is made in short notice of a game, fans who have already purchased tickets cannot elicit their response though attendance (or lack thereof). Thus, in order to test the impacts that players resting has on demand, I start with building a data set on NBA national broadcasts from three sources:

1. Television ratings from Show Buzz Daily;
2. Games which listed a player officially marked in the boxscore as "Did Not Dress" or "Not With Team" from Basketball-Reference.com; and,
3. FiveThirtyEight's CARM-ELO to give a relative value of skill of the teams in each match.

My final product becomes the ratings, number of viewers, and the teams' skill level of each nationally broadcast NBA game in the 2016-17 season (up to April 3rd). I identify 208 games with 68 occurrences of at least one player resting. I then use a model which I have described in a previous blog post for estimating NFL national broadcast viewership.

(1) viewership_i,j,t= f(DNP, Elo_i + Elo_j, X_t)

where Elo_i + Elo_j is the sum of the Elo scores of each team and X_t can be thought of as a set of game-specific variables including the day of week of the broadcast or what network the game aired on. Note that I have separately considered the viewership to be either the broadcast's ratings or the number of viewers (ratings is the percentage of all televisions - on or off - that are watching the broadcast, viewers in this case are the number of adults aged 18 to 49 watching the broadcast).

Finally, our variable of interest, DNP, is a dummy variable which takes a value of one if a player had sat out due to rest. I consider a player to be resting as follows:

• Player must not be designated as inactive (i.e. not injured).
• Player is either
• listed as "Not With Team" and is a member of the away team.
• listed as "Did Not Dress" (can be member of either team).

Through experimentation of model 1, I have found that there is some evidence that suggests the broadcast ratings do in fact suffer when players are rested. The robustness of such a model is certainly up for debate however I have tried several specifications and found generally, NBA games in which a player does not play due to rest result in a 5% decrease in viewership. Some of these results are significant up to a p-value cutoff of 0.1 to 0.5.

Below is a graphical depiction of one possible version of the model. Here I do not control for resting players and plot the predicted values against the actual. I have coloured the games with a resting player with orange (my attempt at little basketballs). Points that lie above the black 45-degree line suggest games in which we would have predicted more viewers than actually occurred (conversely, points below the black line had more viewers than expected).

Note the frequency of which these orange dots appear in relation to the black line. It appears that the majority reside above, but there are still many instances of observations on or below the 45-degree line which calls into question the causality of this correlation. This pattern exists when utilizing different variables for the regression, including team fixed effects, network fixed effects, day-of-week fixed effects, etc.

For a future study, a meaningful variation of this analysis would be to consider the effects of an injury holding a player out of the lineup in comparison to the team's decision to rest a player. Additionally, I have not considered the quality of the players who are sitting out. As Golden State Warriors player Kevin Durant suggested, "[fans] don't care if the 13th man on the bench rests ... it's only for like five players." (Although some of the players Durant went on to list do not often rest, if at all.) This analysis could use a measure of the resting player's productivity to test Durant's conjecture that the demand further declines when high-quality players rest.

I have identified some evidence that resting players may have a negative impact on the demand for basketball of a 5% decline from the predicted ratings for television broadcast of games where a team rests a player. These results have not yet been robustly tested and the estimated effect has quite a bit of variation, ranging from -10% to 0% impact on television ratings. Yet, the public reaction from the NBA suggests that they perceive any decline in demand to be detrimental to the game. The commissioner of the NBA, Adam Silver, has communicated with the 30 teams of the league that resting is "an extremely significant issue for our league." However, considering the size and confidence of that the effect of resting players has on the ratings of NBA games, it may be a little early to introduce new rules to address for this potentially overstated issue.