A Surge!

It’s been a while friends. Sorry for my absence. Hopefully you all have been enjoying the podcasts instead (just search for Crowd Wisdom Sports).

Just wanted to say thanks to user meausa on Reddit who steered a lot of traffic our way in response to a post he submitted detailing his betting results. Quite a few people signed up, though maybe half submitted predictions. Still, it nearly doubled our prediction average, so we’ll take it!

To all you newcomers, thanks for joining the crowd! We hope you have fun (please feel free to e-mail if you’d like to see something – you all are like GOLD to us; we do our best to accommodate).



Postseason Contest Standings (Round 2)

We have updated our contest standings and have a new overall leader!

Postseason Standings

Overall Leaders:

  1. Bryan, 31
  2. Jason, 30
  3. Ellie, 30
  4. Garrett, 28

Divisional Round Leaders:

  1. Bryan, 15
  2. Garrett, 14
  3. Ellie, 14

Week 7 Deep Dive – When Does Size Matter?

I want to start with one of my favorite “Simpsons” clips ever:

After writing the “Does Size Matter?” post, it occurred to me that the rolling average charts we’ve been posting could be considered as being sorted using an arbitrary X-axis. What I mean is that we calculate the rolling average over time, but of course there is nothing that says that time is the factor that affects the change in average (in a later post, we’ll try to correlate changes in predictions to other events, but we haven’t hit a critical mass to do that yet). I, like Chief Wiggum, just put the predictions in an order and try to draw a conclusion.

Moving average when ordered by Baltimore scores in ascending order
Moving average when ordered by date

On the one hand, time is a crucial aspect for predicting the next week. On Sunday night, the previous game is still fresh and the crowd responds to whatever it saw. Then, as news about injuries and such emerges, the crowd responds again. Then on the eve of the game, the crowd has the most information it can have. To that last point, you could assume that the predictions submitted the day before the game would be the most informed. The counter to that, of course, is that the betting lines would move along with any new information so the more informed predictions may not reflect any additional value.

On the other hand, these types of events – the end of a game, the injury reports, the game previews – may also allow for over-indexing. An exciting end of a game in which one team miraculously pulls victory from the jaws of defeat may cause a lot of predicted wins for the victor and a lot of predicted losses for the vanquished. But as the week goes on, perhaps the circumstances of how the two teams ended up there may bubble to the top, and the crowd may resurface the circumstances that required the miraculous comeback in the first place.

All of this leads to the broader question of when is a crowd big enough for us to feel confident in its predictions. According to some basic statistics and survey articles, a 95% confidence level requires about 400 respondents (we’re not trying to separate demographics at this point). And while we’re a far cry from that number at the moment (hey, tell your friends!), once we do get there, it’ll be interesting to explore the circumstances around groups of predictions. Like political polls after a given event, we would likely see swings in one direction or another, but unlike with politics, the common threads that swing the crowd in one direction or another

We’ll explore this more as the season goes on and try to identify when there are shifts in the crowd and whether they mean anything one way or the other. We’ll also try to correlate them to line movements and see if there is a way to identify just when the maximum value is reached.


Week 6 Prediction Analysis – The Best Bets

One of the most valuable opportunities we believe that the wisdom of the crowd offers is with the best bet. Our expectation is that when there is a significant gap between the line and the crowd prediction, they offer the best value.

The main distinction with a best bet that we’re playing wit2017-week6-bestbetsh at the moment is when the delta between the crowd prediction and the line is greater than a touchdown.

So far, such a gap only exists with the over/under, and to date the crowd is performing fairly well:

  • Week 1: 5-2 (and 1 push)
  • Week 2: 3-2
  • Week 3: 3-4
  • Week 4: 2-0
  • Week 5: No best bets
  • Week 6: 1-3

Overall, that gives us a total of 14-11 for 56% success. With a small sample size, it’s too early to make any kind of conclusion, but it’s worth keeping an eye on. We’ll add it to the leaderboard and keep tracking them from week to week.


Week 5 Prediction Analysis – The Outliers

I did a quick study of the predictions from Week 5 and caught a couple of interesting tidbits.

On-the-Money Coincidence

This is an amazing coincidence. User M.G.’s On-the-Money prediction was the highest total of all 10 predictions. User M.R.’s On-the-Money prediction was the lowest total of all 10 predictions. While both score combinations were nice combinations of standard scores (20-17 is 2 TDs + 2 FGs vs. 2 TDs + 1 FG, for example), it is interesting that both were at an extreme end of the crowd. It’s also worth noting that the Bears “typical” result was the sum of a series of quite atypical scoring events (1 normal TD + 2-point conversion, 1 TD off a fake punt, 1 safety).

The Only One Not Wearing a Barrel

User I.R. chose the Jaguars not only to win but to pretty much blowout the Steelers with an 8-point win. The majority of the other scores predicted the Steelers to win by double-digits. I’m not sure what this means, exactly, but perhaps it’s worth taking a flyer on the extremes just to see what happens.


2017 Week 3, Game 1 – The Bad Beat

God bless the internet. Today I get to paraphrase one of my favorite lines of all-time, courtesy of Brian Murphy (now of, as best as I can tell, KNBR). I give you an O/U of 79 points before the Rams-49ers kickoff Thursday night.

“You wake up [Friday] morning wearing a barrel, and signing over your mortgage to me, thank you very much.”

I’m sure I’m not the only person who wonders whether their actions did, in fact, affect something totally out of their control, especially when it comes to football. By posting to Twitter the Crowd prediction (Rams by 4, total of 34) ahead of time before the Rams-49ers game, I am nearly convinced that it turned what people expected to be a low- to medium-scoring affair into a barnburner.

Nevertheless, there are two quick takeaways:

  1. Crowdsourcing the scores is about the percentages rather than any individual game.
  2. Crowdsourcing is hard to do for outliers.


The crowdsourcing scores concept is in line with any other gambling strategy. Blackjack and craps strategies are both about maximizing odds against the house over a number of rounds. If you follow the strategy, over time the percentages will normalize, but it doesn’t apply on any given hand. If you’ve ever read “Bringing Down the House”, you’ll be familiar with the story of one of the players losing over $100,000 on one hand even though he followed perfect strategy. Over time, the team came out well ahead, but on that one hand, luck worked against them.

For a game like Thursday in which the 49ers hit on a backdoor cover when the spread is only 2.5 by scoring 19 in the 4th quarter, it can be doubly frustrating because the win for the crowd seemed well in hand, and to have can feel like a win is being snatched from your fingers. All we can say is that we expect the crowd to be right more over time, so stick with us.


It would definitely be an understatement to say that Thursday night’s game was unexpected. The average score for all games through Week 2 was in the mid-40s, and the 49ers had scored a total of 12 points. Thursday’s game nearly doubled the average score, and the 49ers tripled their season points total in a single game.

In Week 2, the crowd went 6 for 9 on the Over/Under when the delta between the O/U line and the crowd prediction was greater than 5. Thursday was the first of 8 games with that kind of delta, so we’re hopeful that we can expect 5 of the remaining 7 to come through.

In the meantime, does anyone have a barrel I can borrow?


2017 Week 2 CPR: Finding the Sweet Spot

Overall, a decent performance from the crowd this week both straight up and against the total. The crowd is still behind in the spread portion, and we expect that as we get more users to contribute predictions, this number will improve.

CPR Week 2 Summary

  1. Straight-up: 12-4
  2. Against the Spread: 5-11
  3. Over/Under: 10-6

CPR 2017 Overall

  1. Straight-up: 20-11, 65%
  2. Against the Spread: 10-21, 32%
  3. Over/Under: 19-12, 61%

The main challenge is still the total number of predictions. We’re still hovering around 5 but are hopeful that success breeds success. Nevertheless, the over/under number is leaves room for optimism.

A One-off Crowdsourcing Exercise

Cynthia Frelund (@cfrelund), who is an expert in statistical analysis, posted a poll on Twitter and solicited scores for the Week 2 Monday Night Football game between the Lions and the Giants. I did a quick aggregation of 50 predictions and the result was Lions 23, Giants 19. The crowd went 3-for-3.

There were some extreme outliers, but even with predictions such as 13-10 or 16-10, the prediction coalesced after about 30 or so.

So crowd, we need to get to 30! Tell your friends! 🙂

Crowd-wisest Prediction of the Week

This is a new feature we’ll be running every week, highlighting the game(s) that the crowd was the most prescient in predicting. The primary qualification is that the crowd has to get the winner, the spread, and the total results correct. After that, it comes down to accuracy.

This week’s game was the Cardinals at Colts.

Winner Cardinals Cardinals
Spread Cardinals by 2.25 (Cardinals favored by 7.5) Cardinals by 3
Over/Under 32 (under 44) 29

We don’t measure success specifically on this kind of accuracy, but it’s always interesting to note.

The larger takeaway is that the deltas between the lines, the predictions, and the results. The Cardinals were favored by 8.5 points, and the over/under line was 44. We believe that, over time, the crowd can identify the lines where the perception of the teams is significantly different from the actual quality, and this is a great example of that.

Crowd-foolishest Prediction of the Week

Of course, for every two wise predictions, we figure that there will be one foolish prediction.

There were two bagel games: Vikings at Pittsburgh and Washington at Rams. I’m discounting Vikings at Steelers because of the late-breaking news that Vikings QB Sam Bradford was not going to play.

Winner Rams Washington
Spread Rams by 4.5 (favored by 2.5) Washington by 7
Over/Under 38.5 (under 46) 47

Clearly, the Rams’ big win over the Colts and Washington’s struggles against the Eagles gave people the impression of a bigger discrepancy in quality than there actually was. In the end, the game was quite close and was only decided in the last few minutes so it will be interesting to see if there is an indicator when the crowd believes a game will be close one way or the other.

Top Performers This Week

Big week by user B.C. who topped all three leaderboards!


  1. B.C. (13)
  2. C.A. (11)
  3. P.C. (10)

Against the Spread

  1. B.C. (10)
  2. P.C. (8)
  3. C.A. (7)


  1. B.C. (9)
  2. G.A. (8)
  3. P.C. (8)

Thanks for all the predictions! Keep up the good work!



2017 Week 2, Game 1 – 3-for-3 Thursday

The crowd is off to a great start this week! The crowd predicted the winner (the underdog), the spread, and the total.

On the other hand, we just had two predictions, so that’s just random chance for now, but sometimes you take your wins when they come.

One follow-up from the KC-New England game last week that I thought was worth mentioning was about decentralization and independence. I browsed the Patriots subreddit on, and I found a thread fielding predictions for the game. All of the predictions (about 20 before I stopped counting) picked the Patriots, and the average score was 31-18. That would have been good enough for the over/under, but it was wrong straight up and against the spread.

Of course, when you have a forum of like-minded people, this is exactly what you’d expect. It would be rare for someone to post a close score let alone the Patriots losing, unless they were particularly brave (or they were a troll that enjoys tweaking a group of people). So, decentralization and independence are critical to ensuring that the aggregated predictions reflect the crowd wisdom and not a herd mentality.



2017 Week 1 CPR: When is a crowd a crowd?

I took a philosophy class (I think) during my freshman year of college, and I still remember a story from that class (for some reason). Socrates places a grain of sand on a table and asks his students whether it is a heap. He adds a second and asks again, then a third and fourth and so on. Ultimately, it is determined that there is no specific point at which a collection of grains of sand is a heap. (This is called the Sorites Paradox for those who are interested.)

I mention this because the crowd had a relatively average week this week:

  1. Straight-up: 8
  2. Against the spread: 5
  3. Over/Under: 9

So, the crowd is better than 50% with straight up picks and against the over/under but well under 50% against the spread.

What do we make of this?

Well, as I mentioned in an earlier post, the wisdom of the crowd is measured over time, so we’re not panicking yet. (As my favorite NFL writer Gregg Easterbrook says, there will be plenty of time for that later.)

But the broader idea that we want to develop is to determine when a crowd is a crowd. This week, we had five predictors. (Thank you to those who submitted predictions!) Is that enough to make a crowd?

For the crowd to have predictive skill, we believe that the highs and lows need to be normalized. This allows biases to be averaged out, and last minute predictions won’t have a strong effect in one direction the another. One game that I recall being affected last season was the Raiders-Chiefs Thursday night game. There were five total predictions, and two predictions came in pretty late that swung the crowd predictions from the Chiefs to the Raiders.

So overall, it would make sense that the prediction truly reflects the crowd when it shows a degree of stability. (I’ll do a more detailed assessment of how to address these statistics in a later post.) As you all continue to provide predictions, we’re excited to see, over the course of the season, when we’ll be able to identify the sweet spot when a critical mass of predictions has been submitted to give us a level of confidence in a given prediction.

Thanks for all your contributions so far. Keep up the good work!



2017 Week 1, Game 1 – A Brief Study

The summary: the crowd was wrong on the winner, against the spread, and on the over/under. (If I were to draw a silver lining, I’d say, “Hey, the crowd was one point off of New England’s actual total!)

This is a concern since we’d like the crowd to be right all the time, but there are two key components to consider:

  1. Our crowd prediction for this first game was small.
  2. Last year, the crowd did very well when the number of predictions was (believe it or not) 8 or greater.

So, the long and the short of it is that the more people we have making predictions, the better our results will likely be.

So please tell your friends! Thanks!