RBD breaks down two WNBA betting picks while explaining why sharp handicapping requires understanding reversion toward the mean instead of blindly chasing trends.
Minnesota at Phoenix
New York at Portland
Two WNBA Picks with Analysis
Before I get to today’s picks here’s a little handicapping tip/reminder.
I talk (okay, admittedly, I preach) a lot about how sports bettors have to be aware of Reversion Towards the Mean.
Too many bettors I talk to like to quote current hot trends and jump on streaks.
Here’s a perfect example of why you shouldn’t.
At the start of the NBA playoffs the Under was on a torrid 12-2 run. That’s 85%.
Then the record went to 30-21, 59%.
Today, it’s at 36-33, meaning that unless they were very selective and very lucky, most bettors who jumped on that Under trend lost money.
THAT is Reversion Towards the Mean.
Learn it. Know it.
Now let’s get down to business.
It’s early in the season so I don’t have a lot of data accumulated yet, but I’m sticking with my preaching that jumping on streaks isn’t how you beat sports betting. Anticipating and getting on one early is how you win.
And that’s what I’m shooting for today.
Here’s what my charts look like for the two handicapping models I’m using for sides and totals.
WF1 is 0-1 overall. Hm record 0-1, Rd 0-0.
WF2 is 1-2. Hm 0-2, Rd 1-0.
T1 is 0-1. Ov 0-0, Un 0-1.
T2 is 0-1. Ov 0-0, Un 0-1.
Yeah, not a lot of data accumulated yet.
Today, four spots qualify, including a match.
Game One
WF2 says Minnesota (Rd team) should be the Fav. WF2 on the Rd is 1-0.
I’m taking Minnesota.
Phoenix is 1-1 SU and ATS, both games on the Rd, beating Vegas and losing to Golden State.
Minnesota is 1-0 ATS. They opened the season as Hm Dogs and lost to Atlanta by one point.
The Lynx had the best record in the league last season and retained much of their lineup.
So why were they Hm Dogs?
They were missing their star Napheesa Collier,
one of the best players in the league.
They were also missing another starter, Dorka Juhasz.
Neither of the two will be back anytime soon which means Minnesota is going to have to learn to win without them, but they’ve got a deep bench and it shouldn’t be a problem.
The Lynx opened as Dogs, +3′, and due to their injuries plus it being the Mercury’s Hm opener, the line has been bet up to +4′.
I think I can squeeze a little bit more out of that number so I’m going to wait to buy this one later in the day, looking for +5 or better.
Game Two
T1 says the NY/Port game stays Under.
T2 says the Atl/Dal game and NY/Port games stay Under.
Both models for totals are 0-1 on Unders, so I’m going to ride the game that has a match, fading NY/Port and taking the Over.
Looking at my charts from last year I can only find one game where I had a match on an Under.
The game went Over.
It looked “off” to me that I only had one game qualify as a match last year yet I’ve already got one this year, on just the fifth day of the season. Looking closer I saw the reason for this – I had stricter qualifying parameters. I didn’t get many plays.
Last season the WNBA had 13 teams playing 44 games each. That means a 286 game season.
And I only had 31 games that qualified for T1, and only 27 for T2.
So I’ve loosened up the parameters this season to get a few more plays to qualify.
Both of New York’s games have gone Over.
Portland’s only game played has gone Over.
Today’s game opened at 173′ and there’s been no movement.
Using today’s total of 173′, both of New York’s games have gone Over, but one needed OT and would have been an Under by four points at the end of regulation.
Portland’s only game came in at 181, seven points above today’s number.
Without a lot of data and/or experience with this year’s lineups yet, I’m only guessing here but I think this number goes up so I’m buying it now.
My plays:
Minnesota (wait to buy)
NY/Port Ov 173′
Recap: 0-1
Record: 0-2
Had New York -6′, they won by 5.
Two losses, one by the hook, one by 1′.
Both plays were “gut feel”, not based on any of my handicapping models. Sticking with my models today and hoping I have better results, despite the lack of data accumulated.
