These wheels come in 38mm Width/62mm Height for indoor skating, so they are a  relatively  narrow wheel.  I wore these wheels for about 3 months, 2 seasons ago thinking I was getting the old version of them when I ordered.  I was surprised with a newer version of a wheel that I did not want changed.  They were great for speed and juking around the track, and for stopping.   (The old version came in a darker Forest Green as opposed to Kelly Green).  I loved the old Green Omegas:  They really were the perfect wheel for me.  As a skater, I tend to skate on my toes, and on the front two wheels of my skates.  I like to accelerate quickly through sprinting, and not use long strides as much.  With the changes, the wheel just wasn’t the same.

What I didn’t like about the changes was that the wheel seemed to wear out more quickly, not be as fast and much less solid in the center of the wheel.  For instance, the “new” omegas have a nylon hub, while the old ones had a hollow core.  The old ones are more like the Atom Lowboys, which I really like, and the news ones, are just like an unwanted relative who annoys you.   With the Nylon hub, it feels like there is too much give or spring when I skate, and therefore, it takes away from speed as I am pushing, yet my energy is going into the Nylon as opposed to into the floor.  Plus, they do not feel as hard as the old ones, yet I can not find the data on the old ones, so I can’t really say if they were perhaps a 95A unlike these that are 93A.

It’s important to note that if pricing lends to quality and performance, (you get what you pay for saying), the Atom Omegas 93A Green are approximately $40 cheaper (list price) than the Lowboys.  So, if you are on a budget, and you are an agile skater, then you might prefer to wear the Omegas.

My request to Atom, bring back the old Omegas.  They were far superior.  Just this gals’ opinion.  However, as an afterthought, given the prevalence of scrum starts, who needs to roll as much?  It’s become more of a game of lateral agility, and they are quite food for this.

P.S.  I dug up an old set of Green Omegas, and I am skating on them now.  I begged someone for them, and since it’s hotter and more humid out, and we skate on wood; they feel like new.  If I were skating on concrete or skate court, I would not be wearing them though.  They are better suited for wood and polished concrete.

http://rolladonis.com/

Sure-Grip Avenger in White:

Before I start the actual review, let me spell out what I think makes for an awesome derby plate:  Durability, agility, maneuverability and roll performance, stopping performance, lightweight, speed and precision.

I want to be able to turn on a dime, and at the same time be able to roll like mad on hard wheels and not have my ankles and legs weighted down by heavy plates.  Of course, I want the plates to last and not break down on top of the rest.

I currently have two sets of white Avengers, a set on my Vanilla Boots and a set on my Riedell 951s.  They are both set up short forward mount.  The Avenger in white has a new DA45 truck, which is slimmer than the original version and optimized for this with the fixed king pin in the Avenger.

Onto the review….

From my perspective, the Sure-Grip Avengers with the DA45 action meet my requirements across the board.

First, let’s look at durability. For those who are nervous about the strength of the Avenger and subscribe to the idea that if it’s lighter, it has more potential to snap or break mid-jam, odn’t worry.  I weigh more than the average skater (most likely) and I practice sometimes 6 times per week.  I have had  no breakdown with this plate since I started skating a prototype in October 2011.  So, Durability, check!

Next, agility, maneuverability and roll performance since they are all intertwined.  Wait…How is roll something to which the plate contributes?  Read on….

There is no skate action better balanced for derby than a DA45.  It can be tuned with various cushions to go from silly-loose as to be uncontrollable or so tight they will not steer.  So, that leaves a lot of tuning room in between.  Don’t get me wrong, any skate can be tuned in a similar manner, yet that is where the DA45 geometry steps to the front.  For any given state of adjustment the DA45 will turn easier and sharper, faster than anything else out there.   On scale of 1-10, you can get any skate to turn into the 6-7 range, although some take more work than others.  The DA45 goes to 10 due to the unique combination of the “45 degree” king pin angle, the precious adjustable pivot and of course the double action cushion arrangement.   The geometry also lets the wheels stay flat on the floor longer than other designs.  This improves traction, which means you can generally run harder wheels.  That’s where roll comes in.

Want a short forward mount?  This plate is perfect for that, and Sure-Grip even writes “your plate will not hang over the front of your boot”, which lets the place be mounted further forward into the derby sweet spot.  This leads to more agility, better maneuverability and roll performance, all there in spades, check!

What makes them so light?  The white Avenger plates are made of magnesium alloy and feature a holl0w center.  The kingpin is hollow as well.  Both of these features were designed to save weight, which translates into less fatigue on your feet, easier pushing and overall lightness.  There are lighter plates out there, but they are plastic.  They bend and twist which costs the skater performance and there is always the possibility of a plastic plate breaking.  It’s lightweight and with none of the disadvantages of most plastic plates.  Check!

Speed, you will certainly have that as well with the Avenger.  The sleek design lends to less air over surface friction, and therefore, less drag, more speed.  You can focus on working smarter, not harder.  Riding on these plates reminds me of driving an expensive BMW, the handling is supreme, and pick up is unsurpassed.  The plate does a remarkable job of sending energy to the front or back, which allows for your wheels to meet the needs of the jam at hand, whether it be quick pick up speed or long strides.  Speed and Precision, check!

These plates have made turning, hockey stopping, snowplowing and powersliding so much easier.  They make juking and running on your skates feel like you are running on air.  So, Stopping, check!

The Avenger in white is starting to take off in my league with skaters  replacing their old DA45 Magnums or what not, and newer skaters getting them on their first set up.  The feedback is pretty awesome.  Never before have I heard so many positive comments about a skate plate!  personally, they have revolutionized my skating, and I can not recommend them more.

This plate runs $191 MSRP.  You can find them on various derby websites and www.suregrip.com.  If you have more questions about “how the (white) Avengers work”, then just check out the website above.

~Athena #7 of the Steel City Derby Demons.

This is going to be the summer of roller derby documentaries. We can add Hell or High Water to our list of derbumentaries. The trailer doesn’t give much info behind the story of the film but here’s what their site says.

This inspirational story follows the Nashville Rollergirls through the loss of their practice space, the move to a new game venue, injuries, relocation of key players, huge financial pressures, and even an epic flood.

Ever the underdog, and with a burning in their bellies, the Nashville Rollergirls seek to prove themselves at every turn. And nothing will prevent them from achieving the goals they set.

On a quest to succeed athletically on the national stage, and to keep their skater-owned and operated organization afloat, the Nashville Rollergirls, in true Nashville spirit, refuse to accept ‘no’, ‘can’t', or ‘won’t’. And in true roller derby spirit, they bring ‘do-it-yourself’ to the next level.

Great photos too.

http://www.wired.com/playbook/2012/03/teenage-roller-derby/

photography by Fotodog

In both the introduction and situations posts, we’ve talked about expectancy on a jam-by-jam basis.  We’ve counted one absent blocker at the double whistle as a -1 toward the iPS.  It’s a decent way of measuring the cost of blocker penalties because the beginning of the jam is when pack strength matters the most.

Jammer penalties, on the other hand, are a completely different beast.  Whenever a jammer is in the penalty box, it’s a very big deal.  The situation in the pack is secondary to the lack of a jammer, so we need a different approach for measuring the effect of power jams.  We need something that tracks over multiple jams and directly addresses the points scored as a result of the power jam.  To this end, I created a stat called Points Off Power Jams, or PoPJ for short.

The goal was to separate out the points scored as a result of the power jam from the points scored as a result of normal pack play.  Anyone can watch a bout and estimate the number of points resulting from a power jam, but I wanted an objective way of tracking it.  There are a lot of different scenarios that can come into play surrounding when a jammer enters or exits the penalty box, so I created a set of rules to address exactly when PoPJ starts and stops counting points:

(1) Jammer lap points are never counted.
(2) Jammer ghost points are always counted.
(3) If the power jammer is between passes when the boxed jammer commits the foul, PoPJ begins tracking immediately.
(4) If the power jammer is in the middle of a pass when the boxed jammer commits the foul, any points scored on the current pass are not tracked towards PoPJ (except for any jammer ghost point), and PoPJ begins tracking as of the next pass.
(5) If the boxed jammer exits the penalty box having already completed an initial pass and eligible to score points, PoPJ stops tracking points as soon as the boxed jammer enters the pack. Any scoring pass not COMPLETED prior to this is not counted (except for any jammer ghost point).
(6) If the boxed jammer exits the penalty box needing to complete an initial pass to become eligible to score, PoPJ continues to track points scored by the power jammer, but only for the current / next scoring pass (except for any jammer lap point).
If you’d like a more detailed explanation of the rationale behind this system, you can read my personal blog post about it here.

Using these rules, I tracked the PoPJ for every jammer penalty in the 20-bout study.  The goal was to measure the average impact of a 1-minute power jam.  It’s important to note, though, that a significant number of power jams are less than a minute.  The power jammer is sometimes also sent to the box, resulting in both teams having shortened power jams.  Occasionally the bout also ends prior to a jammer being released from the box.  I decided to calculate averages both with and without shortened power jams.  Here are the results:

Avg Power Jams Per Team Per Bout = 4.7

Honestly, I’m surprised by how low this is.  I went into this expecting the average to be 15+ points.  After all, the top teams in WFTDA are generally really good and capitalizing on jammer penalties.  When you hear the words “power jam,” you immediately picture a handful of grand slams and a 20-point swing.  The truth is that these teams are as good at killing a power jam as they are at taking advantage of one.  There were definitely a decent number of 19-point gougers, but there were also a lot of 5-point kills.  Some power jams even resulted in zero points.

It’s worth pointing out that the average total PoPJ for a bout is almost exactly 50 points (it’s 49.60).  If a team can manage to skate an entire bout without losing their jammer, they can theoretically expect to gain a 50-point advantage as a result.  Of course, skating an entire 60-minute bout without sending a jammer to the penalty box is nearly impossible.  Every team included in this study gave up at least 2 power jams in every bout.  Still, limiting your own jammer penalties can really help.  At the end of the bout, you want to make sure your total PoPJ is greater than you’re opponent’s.  If you can drive down the number of power jam points you’re giving up, you can make it really difficult for your opponents to outscore you.

The total PoPJ for any team is effected by both the number of power jams a team gets and how well that team capitalizes on them.  By the same token, the opposition’s PoPJ is effected by the team’s propensity for committing jammer penalties as well as their ability to limit the damage from them.  I started thinking about how power jams effect teams differently, and I decided to run the numbers for all 12 teams across the 20-bout study.  Let’s take a look at how they did in these categories:

There’s a lot of analysis that can be done here regarding the teams’ different styles of play.  I’ll let you draw your own conclusions, but I’d like to point out that there are several factors that determine how a team performs with and against power jams.  How quickly does a team recognize and react to jammer penalties?  Which opponents did a team face in these bouts?  Teams have a certain amount of control over how often they send their own jammer to the penalty box, how well they take advantage of power jams, and how well they kill a penalty.  The most difficult thing for a team to control is how often the opponent gives them a power jam opportunity (although good teams can force more jammer penalties).

What I take away from all this is that the cliche is true: Power jams matter a lot.  —Though maybe not for the same reasons as is widely thought.  Yes, one power jam can be a big deal, but on average it’s not that much of a crisis.  The big thing is the cumulative effect of power jams.

In the next and final article of the series, we’ll take a look at the value of getting lead jammer.  Just how important is it?  We’ll also be examining strategy and the effectiveness of poodling.   See you then.

P.S. -  I added this data in the comments section of the last article, but as we were just comparing different teams’ performances as it relates to power jams, I figure it’s interesting enough to add on to the end of this article as well.  The only new stat here is iPA, which is Initial Pack Advantage (own iPS – opponent’s iPS).  I calculated the total iPA for each team in the study so you can get an idea of how often the teams had the pack advantage or were outnumbered.  Take a look:

At the beginning of a jam, each team lines up with 1-4 blockers between the pivot and jammer lines.  The number of blockers each team has as of the double-whistle is being represented in this study by the stat: iPS (initial pack strength).  The number of pack skaters varies depending on penalties, and we’re using the iPS stat and jam outcomes to calculate average results.  In the 20 bouts included in the study, we had a total of 847 individual jams.  Taking into account both teams in every jam, that’s 1694 total samples.

Let’s start out by taking a look at how often you’ll see of all the possible pack situations.  The following chart represents the frequency of each pack situation over the 20-bout study, in percentage of total jams:

Notice that a 3-pack is more common than a 4-pack over the course of a bout. In fact, teams have 4 blockers on the track only about a third of the time.  The most common pack situation is 3-vs-3.

Now that we have an idea of how common each pack situation is, let’s look at the expected results.  PLEASE NOTE: I am excluding jams where any team started on a power jam from the following charts.  The purpose here is to get an idea of what odds are facing teams in different pack situations.  When one team is on a power jam, odds are altered and the averages for non-power jams no longer matter.  By the same token, power jam averages are irrelevant when both jammers are on the track.  I’m also excluding 1-pack situations from the following charts due to the extremely small sample sizes.

The following table represents average jam differentials in non-power jam situations over the 20-bout study:

Check out the differences here between even packs and uneven packs.  It appears that having any advantage is more important than how big the advantage is.  4-vs-3 and 3-vs-2 situations yield very similar results.  With a 1-skater advantage, you are going to average just under 2.5 points over your opponent.  Having a 2-skater advantage actually isn’t that much better.  A 4-vs-2 advantage is less than a point better than either scenario with 1-skater advantage.

Let’s take a deeper look into how teams achieve those differential numbers.  This next chart represents the average lead jam percentages in non-power jam situations over the 20-bout study:

It still appears that having any advantage in the pack is the most important thing, but it’s not as simple here.  The differential for a 3-vs-2 advantage is similar to 4-vs-3, but the lead jam percentage for 3-vs-2 is not as strong.  I suspect that is a result of the thinner packs.  Logically, you would assume that the average time a jammer is stuck in the pack decreases as the pack is thinned by penalties.  Because the average time it takes a jammer to escape the pack is less, one good jammer force out can have a greater impact on who gets lead.  Chances are greater that your jammer will escape the pack in the time it takes the opposing jammer to re-enter the pack legally.  The differentials remain similar because ghost points are still easier to earn than lap points, but lead jammer percentage decreases somewhat.

Let’s take a look at this from another angle.  The following two charts represent the frequency of jam wins and jam ties to each scenario.  You can also look at the opponent’s win percentage to see your loss percentage:

By far the most common score in a jam tie is 0-0.  Jam ties are usually the result of the jammer with lead being forced to simply call off the jam to avoid the possibility of being outscored.  That can result from the opposing pack holding the front, but most often it happens when both jammers get out of the pack at nearly the same time.  The tie percentages start off near the overall average in 4-vs-4 packs.  When 1 or 2 skaters are removed, the tie percentage actually decreases slightly.  As the pack becomes even thinner, however, the tie percentage skyrockets.  I would suspect this is because neither jammer can be held in the pack for very long.

Comparing 4-vs-3 and 3-vs-2 situations here also supports the theory that thinner packs mean quicker passes.  There is a difference in jam win percentage between the two 1-skater advantage situations, and it again favors the 4-vs-3 advantage where packs are thicker.  What’s interesting here is that the loss percentages for these two scenarios are very similar.  The reduction of jam wins in the 3-vs-2 situation is more a result of an increase in jam ties.  This also leads me to believe that the thinner packs are resulting in quicker passes, and thus, more simultaneous initial passes.

So what big thing do I take away from all of this?  Penalties matter a lot.  Yes, it’s a cliché.  I listened to announcers on the Big 5 feed say it over and over again.  Still, I didn’t realize just how much penalties really do matter.  I’ve heard people dismiss the loss of only 1 blocker to the penalty box as not being a big deal, and I had always agreed.  I assumed that if a team has 3 blockers, they can still operate at near peak effectiveness.  It’s not a big problem unless you drop down to only 2 blockers, right?  Scroll back up and look at the differentials for each pack strength.  Overall, the loss from 4 to 3 is bigger than it is from 3 to 2.

No stat is perfect.  iPS doesn’t track blocker penalties that are served entirely within a single jam.  Many carry-over penalties extend into a third jam as well.  Still, I think you can learn a lot by using objective stats.  I have provided some personal analysis here, but there is definitely more theorizing that can be done here.  What is the effect of pack speed?  How does strategy change in different pack situations?  Feel free to draw your own conclusions and share them in the comments.

In the next article, we will be examining power jams.  How much do they really effect a bout?  What is the average power jam worth in terms of points?  We’ll try to come up with some objective answers and weigh them against the “conventional” thinking.

See you next time.  Same stat time.  Same stat channel.

In my opinion the best value for your dollar upgrade is usually replacing your stock cushions. The Sure-Grip Super Cushions are similar to their Powerdyne cousin the Magic Cushions in style but not in price. I’ve heard great things about these but not sure what plates they will work on. If you use these and have an opinion please let your fellow skaters know.

Imagine for a minute that the jam begins with one team holding a 4-vs-3 pack advantage over the opposition.  This team gets their jammer out of the pack first with lead, and ultimately turns it into a 2-0 jam win.  Let’s take a moment to evaluate that result.  Do you think the team performed well in this jam?  Do not forget about the advantage in skaters on the track.  Did they translate that pack advantage into a satisfactory outcome?  What basis are you using to make that judgement?  What do you think one team can reasonably expect to get out of a pack advantage like this?

I’d like to introduce you to the concept I call “jam expectancy.”  Basically, it’s the expected result of a jam based on the situation. —A baseline of what the average outcome of a jam will be.  For example:  A 4-vs-4 jam with neither jammer in the box has a simple, zero expectancy.  Since the odds are even with even packs, the average performance for a team in that situation is +0 points.  The 4-vs-3 situation I gave you above is more complicated than that.  The team in question has an advantage, thus they would have a positive expectancy.  They’re expected to win the jam in that situation, and there is an average number of points they are expected to win it by.

Did I lose you?  Let me backtrack a minute.  There is a baseball statistic called “run expectancy” which is the inspiration here.  Statisticians have taken the thousands and thousands of samples of each situation that can come up in a baseball game (such as: a runner on second with 1 out), and they calculated an average performance for the rest of the inning based on each situation.  The result is a chart you can use to get a pretty accurate idea of the chances of scoring in any situation.  Beyond that, you can use such a chart to evaluate a strategy like a sacrifice bunt (Would it increase or decrease the chances of scoring?).  If you want to see what a run expectancy chart looks like, you can find one here.  It’s a brilliant tool for analyzing the sport as it actually gives you a numerical value to each situation.

Well, I had the idea of doing an in-depth statistical study and gathering some expectancy data for roller derby.

This is the point where I should probably tell you that the data from this study and resulting analysis is the subject of a series of articles for which this is the first.  We’ll to be taking a look at all of the objective data and seeing what conclusions we can draw from it.  That includes examining things such as how pack situation affects average jam results, the impact of power jams, etc.  From that, we’ll be able to weigh conventional derby assumptions and strategies against objective data.

To begin, let me explain the parameters of this study.  Roller derby competition isn’t structured like professional baseball.  MLB teams play 162 games in a defined season, and the level of skill is comparable between all 30 teams.  Neither of those statements is true of roller derby.  Roller derby has no defined season with each team playing a set number of bouts.  There’s a much smaller number of games played each year.  A lot of those games are also blowouts, featuring 2 teams with mismatched skill levels.  In order to come up with some useful expectancy data, we would need a set of scheduled bouts between similarly-skilled teams.

There were 12 teams that made the WFTDA 2011 Championships in Denver, Colorado:  Gotham, Philly, Charm City, Oly, Rocky Mountain, Rose City, Texas, Kansas City, Nashville, Windy City, Minnesota, and Naptown.  You can argue that there were comparable teams that did not make the cut, but this criteria satisfies the need for an objective cut-off point.  In the WFTDA Big 5 tournament structure, these 12 teams were matched up against one another 20 times: There were the 12 bouts in Denver, plus 2 in each regional playoff tournament (each regional championship + the semifinal which featured the eventual third place finisher).  20 bouts is a decent enough sample size where we might be able to get reasonably accurate averages.  I collected the data for this study using the WFTDA Big 5 video archive.

For tracking pack situations, I’m using a stat I created called Initial Pack Strength, or iPS for short.  It’s simply the number of pack skaters each team had on the track.  The beginning of the jam is generally when lead jammer is decided, so it marks the point where pack situation has the greatest impact on the outcome of a jam.  Please note: I tracked the pack strength for each team as of the moment the jammers were released, not on the first whistle.  —This is an important distinction because I essentially adjusted for poodling and for slow-starts that released blockers from the penalty box. I wanted the most accurate numbers possible.  If one team poodles a skater, and the other slow-starts to get a blocker back in, suddenly a 3-vs-3 pack situation becomes a 2-vs-4.  I chose to use the data as of the moment the jammers are released because it’s easy to identify, and it’s the actual situation each jammer faced.

Before we wrap-up this introduction, I’ll give you some data to whet your stats appetite.  In order to understand individual jam expectancy and how it applies over a bout, let’s examine some basic period/bout numbers.  Take a look at how scoring and jam length can change as the game unfolds:

We can see here that the scoring increases in the second period, but the average number of jams decreases.  This is undoubtedly the result of thinner packs.  As the bout goes on, packs are thinned by penalties and scoring increases.  This is partially due to ghost points, but it is also a result of mismatched pack strengths.  When one team has an extended pack advantage, they often stymie the opposing jammer.  Since jams are usually called off to prevent the opposing team from scoring, having one jammer stuck in the pack more often leads to longer jams and more points being scored.  The same thing goes for power jams, which would logically increase in the second half as penalties mount.  Also, another factor here could be large point-spreads.  If one team has a significant lead in the second period, they might choose to simply bleed the clock and let longer jams play out.  The longer the jams run, the fewer jams can fit into a period or a bout.

In the next article, we’ll start getting into the meat of the data.  You can expect a breakdown of individual pack situations, how often they come up, and the expected results from them.  This is going to be fun.

Guess what. We need someone to write us a nice long review of these wheel. Give us your opinion and make your voice heard. Info@QueenOfTheRink.com

Since I posted the video, I’ve been teaching the concepts around those 10 starts I published on YouTube back in October of 2010. With more than a year past now, it’s refreshing to find the lessons learned in these jam starts are still relevant today and we’re only building from here. Look for more videos coming soon including a graphic and video version of my jam starts article published last summer in Five on Five as well as the continued evolution of jam starts with video from World Cup 2011.

I split this “10 Jam Starts” video into two parts due to length. It helps to watch them in order as some of the latter seven jam starts have commentary that reference the first three.

Part 1

Part 2