We briefly mentioned a bit about advanced stats yesterday. This is something we haven't really paid attention to until recently, but looking at them just makes so much sense. We might end up going all in on these stats.
Advanced stats from the Pensblog? This is like a monkey learning how to pedal a bike. It's funny because we think we're people.
Obviously, no statistic can't tell you everything you need to know about a player or a team, but some advanced stats are certainly a good guide. They're also a great way for dispelling myths about certain players who seem to be praised and highly-valued because of their "heart" and "importance to the team" when they don't seem to ever contribute much. They can also show why some players are highly-valued despite displaying less than great numbers in traditional statistics. You can also use them to determine the strengths and weaknesses of a team. They may also help you build a rocket to Mars and establish a civilization there, but we're not sure about that one yet. We're just learning.
JibbleScribbits, a Colorado Avalanche blog, has put together a good introduction to advanced stats. You should read it, even if it's just so that you can decide that you don't care about advanced stats. More after the jump.
As many here have seen here hockey is starting to warm up to fancier statistics. A ton of really smart, advanced statistical analysis has been done by many people over the last 5-8 years that has seen a great deal of new useful statistics (Corsi, OZone%, PDO) replace more traditional statistics (±) in effective player evaluation.
One of the key points that really gets lost is that the only math you need to know to understand these statistics is: +, -, x ,÷. Yes, there was a lot of advanced statistical mathematics that went into the development and checking of these stats, but that math is completely unnecessary to put them to use in day-to-day player and team evaluation. I will put some links to some posts that show the math at the bottom, so if you really want you can check it out, but knowing the math behind the stat isn't necessary for knowing what the stat measures.
And that's a key point, a statistic is a measurement. The better the statistic (and the more events that make a statistic), the better the value. Many have heard me say ± is useless, and it's because using ± is like using a sundial to measure a second, or an unmarked yardstick to mark off an inch. The fidelity of ± is so poor that there's no fidelity to the measurement. It's worthless. Luckily for us, some people have developed some statistics that provide a ton more fidelity.
Our friends at Pension Plan Puppets have a good introduction to advanced stats as well.
We don't 100% know what we're talking about yet in this area, but we're learning. We'll have more later once we figure things out. Or maybe we'll Photoshop some NHL players into 90s wrestling scenarios. Or both.