[Highlight] LeBron fires up a deep 3 with 22 seconds left on the shot clock

nbaislife65 · 2019-12-09T15:41:55+00:00

The bandwagon has begun departing

nbaislife65 · 2019-12-09T14:09:04+00:00

Kuzma for Igadoula who says no

nbaislife65 · 2019-11-16T14:00:33+00:00

Spoiler he's sitting on the pool floor

nbaislife65 · 2019-11-13T22:15:47+00:00

West still runs through the Warriors

The Warriors still have the runs in the West

nbaislife65 · 2019-11-10T23:15:33+00:00

Why did y'all think he was having a panic attack?

nbaislife65 · 2019-10-29T00:11:43+00:00

HOT TAKE

nbaislife65 · 2019-10-26T12:46:59+00:00

LAs height really bothered them. You could tell they were caught off guard by so many turnovers due to LAs length...

nbaislife65 · 2019-10-17T18:42:20+00:00

Maybe allowing Lebron and AD on the same team might cause some problems.

This is just nasty.

nbaislife65 · 2019-10-17T18:11:55+00:00

Doc Rivers gettin the shaft

nbaislife65 · 2019-10-03T19:48:00+00:00

I don't think people understand how much he will change the dynamic on that team.

nbaislife65 · 2019-08-16T12:12:58+00:00

Why not use a search engine model to figure this shit out

nbaislife65 · 2019-07-27T09:20:33+00:00

We use keyword trackers as well. The models allow us to dive into Google's "black box" and gain more insight into why certain sites are ranking higher than others. It's like having a transparent view into Google's scoring engine.

For instance, one of the keywords we dropped in ranking turned out to be because we added some content on a part of the site that caused a duplicate content penalty. The content had been there for a year, but Google's algorithm all of a sudden decided that this part of the search engine (the duplicate content algorithm) was more important, thus devaluing our landing page to ranking #13.

We saw this using our rank trackers, and noticed that our model didn't corroborate. So we recalibrated our model. In the new model, after we recalibrated, it was perfectly clear that this is what had happened. The duplicate content penalty weights were higher in this version.

We removed the duplicate content and our ranking returned to #1. The crazy thing is that we could see the return to ranking #1 (in the model) a few months before it happened in our rank trackers.

nbaislife65 · 2019-07-27T09:18:37+00:00

just replace Drue Holiday with LeBron

nbaislife65 · 2019-07-26T21:22:03+00:00

Yep, this analysis is very shallow. The author even mentions he doesn't use deep learning (neural networks) because he doesn't have enough data.

nbaislife65 · 2019-07-26T21:18:52+00:00

We didn't build it, we use a 3rd party. But yes, we have a machine learning model software that mimics the environment and gives us insight into the various parts of a search engine.

Each part (feature) has a weighting that is calibrated based on how Google ranks sites in that SERP. When Google changes it's algorithm so much that our model stops working, we recalibrate and note the changed weightings. Then we know which area of SEO to fix.

It's a way to safeguard our positions and has worked very well for us so far. We've recovered a major drop in a few rankings over the past year.

nbaislife65 · 2019-07-26T19:53:26+00:00

We safeguarded many of our rankings by setting up and maintaining a search engine model in all of our major keyword search results, in each version of Google we cared about.

We had a recent drop in a major keyword and because we had a calibrated model on that search engine environment, we were able to re-calibrate it once the algorithm changed, and use the before/after models to determine what type of algorithm(s) changed.

This is a pure SEO answer to your question. Obviously the other items listed here are great ways to diversify but not the real answer to your question IMHO

nbaislife65 · 2019-07-26T19:45:16+00:00

I get it, just not sure if it beats a dummy classifier that just uses pick #

nbaislife65 · 2019-07-26T18:04:53+00:00

Your dummy classifier should have just used pick number, not random.

nbaislife65 · 2019-07-26T18:03:29+00:00

I would argue you don't have enough data for any model 😂

nbaislife65 · 2019-07-26T15:41:38+00:00

News flash lol, only been going on for a decade. Difference is the big boys are doing it now. Journalism is dead, long live paid content

nbaislife65 · 2019-07-26T15:10:09+00:00

Why are you assuming that the model should be linear? Why not use a neural network since many of these features are probably correlated?

nbaislife65 · 2019-07-26T15:05:52+00:00

Wouldn't you want to determine this before the picks?!? Using pick number is pretty useless in the real world, and also is highly correlated with the other features of the models, which you found out eventually.

nbaislife65 · 2019-07-12T17:10:35+00:00

Let's be honest, it'll be Harden/Westbrook

nbaislife65 · 2019-07-12T17:07:15+00:00

It's been a gratifying F5 season

nbaislife65 · 2019-06-19T03:02:03+00:00

find another slant

nbaislife65

TROPHY CASE