Germany:
Syntainics/Wurzburg u173 WIN +1 [ez]
Bayern/Oldenburg o166 WIN +1 [ez]

Spain:
Baskonia -5 WIN +1 [not so ez]

Italy:
Milano -8.5 WIN +1 [feels good to benefit from a 30-11 4th quarter]
V. Bologna/Treviso o167.5 WIN +1 [almost blew it after 93 point 1H]
Cremona/F. Bologna o164 LOSS -1.1 [ez L]

Today: 5-1 +3.9u
YTD: 58-43-2 +10.7u on 113.3u risked (9.44% ROI)

First 100 Euro bets in and looking pretty good. Couple comeback wins. Lot of fraud losses. 57.42% winners. 9.44% ROI. Lets keep it going for the next hundred Sabernets.

I looked and nothing on tap for tomorrow so we can sleep in.

Tuesday set your alarm clocks. 5 euroleague games, 2 eurocup, 5 champions league, 3 germans, a france, an italy, and a spain.

I guess no one told Hurts that the safety rules are the same in the NFL as at Oklahoma and Alabama.

rip jalen hurts career

2020 week 14-2020 week 15

http://www.somelifeblog.com/2008/05/...kers-shot.html

can't believe this was 12.5 years ago

still tilts me to this day

Caron Wentz is gonna really need the strength from his bible study group

Good cappers incorporate the free tacos promotion into their model. I guess you were still learning the ropes... ha

406 yds pass, 3 pass TDs
Hopkins 9rec 169 1 TD

Hopefully his $108m guaranteed deal that starts next year helps him with that.

Hurts with 3 fumbles, loses none. Expert play!

This jogged my memory of a bad beat I took. Drexel vs James Madison 2009 CBB. I was going through CBB on matchbook looking for offers that people had left up and forgotten about after line moves (not even chasing steam, picking off stale offers that could've been an hour old, lots of CBB games so sometimes they would go unnoticed).

Drexel was +5 or +5.5 and had moved down to +2 or so (don't remember exact lines). Someone had left up 3 or 4 thousand on Drexel ~+5 so I excitedly took it all. As game time was nearing I was honest with myself that I was overexposed so I bet something like 1.5k on JMU ~-2 even if it wasn't +EV just to minimize risk.

Yadda yadda games happens JMU is up 64-60 with 11 seconds left. Drexel misses a three with six seconds left and JMU grabs the rebound. Game over and I win the middle right? Nope JMU tosses the ball down the court for a dunk as the clock expires. 66-60. I was so disgusted at the time I screenshotted and stored it in my photobucket account, which somehow still works. Attached is that screenshot.

I'm still disgusted by the NBA playoff game some years ago [Dallas?] where the losing team dunked it at the end and the ball was fully through the cylinder when the clock hit 0:00 for the over but the refs just walked off and didn't even look at it since it didn't affect the game.

Of course the worst bad beat ever was the Duhon heave 3, right?

Have Rodgers, Hurts, Tannehill as my fantasy QBs, somehow started the guy who scored 20+ pts less than the other 2 but still a fave to win with Ebron to fade.

Thank God Chris Hansen was back on Redzone, Siciliano was getting intolerable

https://www.nhl.com/news/nhl-2020-21...rt/c-319848474

NHL 2020-2021 season schedule plans have been published.
There will be 4 realigned divisions (North, West, Central, East) , regular season games will be played within the division only, top 4 teams per division advance to playoffs.

In the regular season each team plays the other teams 8 games, in the Canadian division 9 or 10 games.

If I understand correctly the first two playoff rounds are within the division as well, meaning that we'll get one playoff semifinalist from each division.

Good timing as there is not much going on today. Couple CBB games, our NBA 1Q and 2H model is ready to go for tomorrow, and a horrible NFL game. So we have time to think about this NHL data.

Where I always like to start is to come up with some questions. I start by trying to read what research is out there. Spent a couple hours this morning and its kinda eh. Most people seem to be concerned about the events after a face off (e.g., does winning a "clean" faceoff increase chances of scoring, faceoff success compared to winning).

Someone (Liardi and Carron, 2011) did a home field advantage analysis back in 2011 that showed home teams have a slight advantage (I read on a couple website that home players get some advantage in how they can setup vs. away which was mentioned in this thread as well).

So not much help. I don't know much about the sport either, but I tried to come up with some questions we can try to answer which might influence the faceoff. And ways to study it.

1) Home field advantage. Does it matter?

2) Do players get worse over time? Possible solution: X-faceoff running average. Sample Y players - see if the decline if significant. If we choose an appropriate X short term fluctuations (e.g., going up against better people for a couple games straight) shouldn't matter. We might have to separate this out by home/away and vs. hand which could make the sample size pretty small.

3) Handedness. Does it matter? I noticed an abnormal amount of people doing faceoffs are left handed which makes me think that it does. Can compare L vs. L, L vs. R. Maybe try to determine an advantage one should get in a scenario if it does matter.

4) Does location of the faceoff matter? Related: Does an opening game neutral zone faceoff differ from a mid-game neutral zone faceoff? Might not have enough data to determine this, but maybe a team has an advantage after scoring a goal? I think we should exclude this and only focus on neutral zones because I don't have the pbp data so I can't tell if teams were at full strength in all faceoffs and if that matters.

5) Might be excessive, but does height matter? We have a players database. The API has the players height. Can send new calls and record their height.

These are pretty basic questions due to my lack of knowledge about the sport + it's just one event in a bigger game. Any other questions people can think of?

What I like to do next is develop data visualizations and summary statistics to try and address these questions. I usually use Python, but I realize the learning curve on Python is pretty high, so for this lets use R. We can build an R Shiny App which is pretty easy. I'm envisioning it will just mostly be tables with options you can select outside of the running average which we can put on a time series graph.

To get that graph we need to merge our schedule file with the faceoffs since the schedule file has the dates. Lets get to work boys.

Anyone have any insight into (initial) betting limits at the following domestic books?

Betamerica
Pointsbet
William Hill
Barstool
The Score

I put the domestic books on the backburner for awhile to focus on some other opportunities and because I was out of Colorado a lot but after refocusing on Fanduel and Foxbet and having limits cut, looking for some new outs.

not sure but i heard kambi is starting to link accounts across books now

i know 2 people who got limited at barstool without placing a single bet. their limits were the same as their DK accounts

****. That isn't good. Happened to me offshore already too. Actually got banned before even depositing in that case.

This question may actually be more complicated than you think. Most players take the face-off on their backhand with a grip they don't ever use otherwise. But what's annoying about modeling it would be that not everyone does. I don't know how you could get that information, especially since I assume some players switch tactics depending on the situation (defensive vs. offensive zone, or whether they have a specific play drawn up). And barely related is that most Canadians learn to play left handed, but most Americans learn to play right handed. (At least this used to be true. And I'm not sure how Euros break down by handedness.) So if you're naturally right handed and also play right handed, maybe you'd have an advantage over some other iterations of handedness in some scenarios.
Location definitely matters, especially since many face-offs are the result of a play that brings with it some other advantage. For example, if a team ices the puck, the face-off is always brought to their defensive zone, PLUS a consequence of icing is that you aren't allowed to substitute players, and those players are likely to be especially tired. And whenever you commit a penalty, the face-off is brought to your defensive zone, and you will have one less player on the ice than the offensive team. In general, neutral-zone face-offs are pretty neutral in terms of who has an advantage, in contrast to defensive-zone face-offs where the attacking team usually has many advantages.
Rule changes may degrade the signal in much of your data. I don't remember how many games you're getting data from, but if it's from many years, some of the stuff before a significant rule change may not be valuable. I think in 2017 there was a rule change that refs were instructed to be more strict on players squaring up properly and staying behind the lines. I think in 2016, they changed the rule of who has to put their stick down first (thought to be a slight disadvantage) from the visiting team to whichever team was in the defensive zone. And for last season, they added a rule that says the attacking team gets to choose which dot they want the face-off to take place at when they have an offensive-zone face-off (presumably to increase scoring).

Another thing to be aware of is that players get kicked out of face-offs pretty regularly (you see it multiple times a game). I don't know if the data you have reflects this. So say you have your top guy about to take the draw, but he tries to get an unfair edge by sliding his skate a little too close to the dot, and the ref boots him. Now you have to use whoever your next best guy is on the ice to take the draw, with the further disadvantage that if he gets booted, it's a 2:00 penalty. So I'd be worried that your data may not reflect how often a top guy gets booted and therefore hurts his team's percentages, since this act may not show up as him actually losing the face-off. I'm sure some players get booted much more than others.

I hate to introduce all these further complications into your model, especially since I don't have a good sense of how significant a lot of them are, but such is the nature of the face-off modeling problem.

12/22 Euroleague

We've run I believe 5-5 on picks that disagree with sig's model and 7-2 on one's that agree. Very small sample sizes, to be sure, but results that I would expect so I might just start betting only my picks that agree with his. Which is cheating I suppose. My fair lines for tomorrow:

Zenit -7.5 vs zvezda (pinny currently -7.5/-8)
Barca -10 (???) vs efes (pinny -3)
Panathinaikos +5.5 vs tel aviv (pinny +7)
Lyon +3 (???) vs Moscow (pinny +8)
Alba +10 vs real (+11.5)

hit on Hansen yds, long rec, td but lost receptions go figure. tonight I don't see much. Action play on james washington long rec over 14.5

thanks for NHL comments. Interesting to think about. Almost got the R dashboard up. Running into a weird issue with date range. Never tried it before and can't seem to get it work. Then I was watching some guy explain it on youtube and got distracted.

Here's my NBA totals predictions for tomorrow:

brooklyn 119.17 pts, gsw 111.41. LAC 110.01, LAL 112.89

which matches pretty well with the market. NBA is unbeatable. Unless you can scam 1Q off rotations and 2H off game situations.

excuse me, i take umbrage being described as a scammer.

intern reporting for duty. PMs open for any coffee requests or assignments

have to scam. dfs people ruined nba by giving out all the data for free. can't listen to radio shows anymore trying to figure out who greg popovich is gonna play and for how long

killed so many livelihoods

unrelated here are some free european basketball picks

Spain:
Canaria -5.5

Germany:
Ludwigsburg/Chemnitz u171

Eurocup:
Busapor/Nanterre u166.5
MoraBand/Bologna u158.5

Champions League:
Hapoel -5.5
Zaragoza -5.5
Strasbourg +4.4
Strasbourg/Peristeri o155

Euroleague:
Panathinaikos +6.5
Panathinaikos/Tel Aviv u156.5
Barcelona -3.5
Zvezda/Zenit u147.5