Sentences with phrase «game sample size»

Two games is not meaningful, we have a 600 game sample size that says good teams are bad bets when they play the worst teams in the league.

But in that same 18 - game sample size, Oklahoma City has only one win against a team in a playoff race.

At the 200 - game sample size, you would need a winning percentage in the low 60 % range to prove statistical significance.

A three - game sample size is a blip, but the Andrew Wiggins we've seen so far looks an awful lot like the Andrew Wiggins we've been waiting to see since he came into the league.

Not exact matches

It just means that there is too much randomness in the small sample size and that the system should be tested over more games (a longer time period or larger sample size).

On a typical NFL football game, you can expect over 20,000 bets to be placed at each sportsbook, resulting in a sampling size of well over 100,000 bets.

Large sample sizes allow you to more accurately observe advantages that you may hold over the sportsbooks, yet it never ceases to amaze how much stock bettors will place in the performance of a team over the past five games.

This system has a sufficient sample size (139 previous game matches), shows an edge over other similar games, and has an underlying theory to guide our prediction.

Despite all this, is it not a bit outrageous to say Sudfield should be in from a sample sizes containing HALF a game?

From that stretch of games» sample size, the one constant was McLeod whiffing and definitely not playing like a veteran safety

A sample size of just a handful of games can only tell us so much, but let's take what we can.

I know, I know, small sample size, blah, blah, blah, but from March 1 on, Wall averaged 23 / 5/8 with fewer than three turnovers a game while shooting 47 percent from the field and a not - terrible 34 percent from 3 - point range.

(Small Sample Size Alert) Mahomes only threw 4/35 of his passes to TEs in the Denver game compared to 6/25 to running backs and 25/35 to WRs.

Before you hit me with the «small sample size» explanation you're about to, realize that Paul has played in 28 games this season — more than half the games the Rockets have played this season.

Since when is over 5k games not a big enough sample size.

Also back to the sample size of 13k games with.1 % roi.

The optimal level for betting road dogs would appear to be at the 25 % level where we see a 4.2 % return on investment and a representative sample size with 131 historical game matches.

We caution that, although these results are based on thousands of spring training games, this is a smaller sample size relative to our complete baseball database (which now covers almost 20,000 data points over eight seasons).

In full disclosure, there are still nine days worth of MLB games to be played this April that will directly influence results and, even after April is completed, analyzing fewer than two month's worth of results is hardly a sample size large enough to draw statistically significant conclusions.

By looking at teams who lost their previous game, our units won drops from +30.18 to +26.02 while our sample size shrinks by nearly 48 %.

Our system now has the significant sample size (143 games) and consistent returns (just two losing seasons in ten years) that you'd look for in a winning betting system.

The graph by temperature is interesting although it's still a small sample size of games for a relatively small difference in winning percentage.

Vert small sample size but I was very impressed with Campbell today and truly believe it is his time to get a run of games.

I concede that three games are a very small sample size to make definitive prognostications about our beloved Atsenal this season, however its very difficult from my vantage point to discredit the overall sentiments of the original post.

Reverse line movement at the 40 % threshold produced a 10.4 % return on investment (ROI) with a significant sample size of 178 games.

While this sample size is incredibly small and not something we'd recommend solely using as a betting system, favorites clearly outperform underdogs when playing bowl games with new coaches.

Rhys Hoskins +5000: Hoskins did for the Phillies what Judge did for the Yankees, just in a smaller sample size... and the games didn't matter, either.

Social networks are increasingly getting their hooks in the live sports streaming game, but we've seen a limited sample size for what baseball can do on a social platform.

With the usual caveats of small sample sizes (nobody with more than 19 games, only 7 teams to compare.

Since the sample size decreased by 315 games, our return on investment more than tripled from 6.2 % to 20.8 %.

With a sample size of over 1,200 games, consistent year - to - year results (with the exception of last season's -2.25 unit loss), and broad ranges for our data, this system fits all three traits for a winning betting system.

They've actually done quite well in this situation over the tiny sample size, going 7 - 3 ATS, but I wouldn't factor in a handful of covers from a decade ago into this week's game.

Our system now has a significant sample size of nearly 500 games and solid returns but, like a spoiled child on Christmas morning, I am never satisfied.

Unfortunately our sample size is too small to extract much from the top trends during the Final Four; however, we do have a number of sharp money indicators for Saturday's games.

It's a small sample size but teams getting less than 40 % of bets in National Title games have gone 5 - 1 ATS (dogs 4 - 0 ATS, favorites 1 - 1 ATS).

I don't know if it was small sample size or concerted strategy last game, but the Caps were ready for it.

Obviously one game is a small sample size and all sorts of disasters could await on the horizon but there is hope that us Suns fans could have found respite from the harsh desert of suckitude and can finally enjoy a midnight at the oasis.

Calipari has the most games under his belt and his profits are based off long - term, consistent success rather than a small sample size.

I literally said that this is applicable to smaller sample sizes, like individual games, groups of games, or a single season.

Because the night game on Thanksgiving is a normal Thursday night game, we feel it fits into our Thursday night analysis and includes a much bigger sample size.

This seemingly minor adjustment increased our overall sample size by 1,183 games while also improving the system's ROI.

That net rating of 33.7 is better than any other lineup the Bulls have deployed this season, though you have to account for the small sample size of just 44 minutes played together over just four games this season.

Ignore small sample size statistics like Team A is 6 - 1 in their past 7 night games against divisional foes.

We understand this is a small sample size and shouldn't be blindly followed, but it's reasonable to theorize that since Thanksgiving Day games provide the shortest week of preparation and game planning all season, they force teams to rely more on talent alone, giving the advantage to the better (or favored) team.

With a sample size of over 4,000 games, I found that the under had produced a 3.8 % when both pitchers had a walk rate of 7.0 % or lower.

Although it's a small sample size, the line moved with the money in 28 of those 43 games (65.12 %).

This was still meaningful information due to the massive sample size (over 13,000 games) and consistent year - to - year results.

Despite our sample size dropping from 182 games to 75 games, the number of units won actually increases from +14.83 to +17.6 and the return on investment sky - rockets from 8.2 % to 23.5 %.

Small sample size but only about 13 % of games have been decided by 3 so far this year.

Do we make too much of the tiny sample size that is a national championship game, especially since those games are assembled in part by generations worth of human assumptions and months worth of confirmed bias?