The [ppm] eyrie

June 3, 2009

What the hell are these numbers!?

Filed under: PPM.statistics, Uncategorized — glanvalleyeaglets @ 10:30 am

Honestly, would you say you believe in statistics? I was taught to distrust any statistics that I didn’t make up myself. (98.3% of all statistics are actually made up.)

When I was younger, I was forced to have some lessons in the noble subject of mathematical statistics after my epic fail at the oral exam in probability theory, so I can hardly be accused of being in love with the matter which Laplace has called “common sense reduced to calculus”.

So, do you believe in statistics? Does the old sailor believe in the drag of the wind? Break the wind down to the molecules and you’ll see miriads of them releasing their kinetic energy to the wrong side of the sails. Break the great laws of Powerplay Manager down to single games and you’ll see nothing but Random. I bet I’ll deny the cosmic order when the mighty Random hits me in midair at the least appropriate time and that will surely happen provided that this is a part of the cosmic ppm order.

The topic is – how do we measure the weight of the wind given the molecules?

Let us start with an assumption. The game results are computed by some sophisticated, possibly probabilistic algorithm. (No malevolent demon or creature making fun of our feeble attempts to understand the natural laws.)

Consequently, given the values of all the visible and hidden variables that can influence the game, including but not limited to the skill attributes and seasonal energy of all the players engaged in the game and the tactical options chosen by the managers, there exists a well-defined probability for each event expressed in terms of result of the game, e.g., the probability of the event “team A wins” or of the event “the total number of shorthand goals scored by both teams in the game lies between 1 and 3”. The probability exists but remains unknown to us.

Let us first consider the idealized case that all teams are equal. This is certainly not true now and will be even less true in the future as the difference in rates of team development plays a more and more prominent role. Suppose that we observe n games, where team with tactics A plays vs a team with tactics B. Let us choose an event, e.g., that team A wins or that the game ends in overtime. Let us define the random variable X_k, which takes the value X_k=1 if the event happens in the k-th game and X_k=0 if it does not.

How would you estimate the probability of the event from the data (X_k)_{k=1}^{n}? Of course, you would count the cases when the event happened and divide by the total number of the games, i.e., you’d take the sample mean M:=\frac{\sum_{k=1}^nX_k} {n}. Let us ask the obvious question: how good is this estimate?

Evidently, the random variable nM is the sum of n Bernoulli random variables and hence has a binomial distribution. Let the true and unknown expectation of X_k for any k be p, then M has the expectation \mathbb{E}(M)=p and variance \sigma^2=p(1-p).

By the Central Limit Theorem we know that the random variable
Z:=\frac{M-p}{\left[M(1-M)/n\right]^{1/2}} \to N(0,1), n\to \infty
asymptotically (for large n) tends to the standard Gaussian distribution. From here we can express
but we know that Z \sim N(0,1) that gives us a weapon to compute the confidence intervals for our estimates!

For example, there is a 95 \% chance that Z\in (-1.96,+1.96), see here for other values (in this table you should look up the value of half of the probability, e.g., \frac{0.95}{2}=0.475 since the integration starts from zero.)

Let us assume that M=0.4 (a typical value in our statistics study), then for n=200 we get a \pm error of 0.0679, i.e., less than 7\%. For n=1000 this 95\% confidence interval halfwidth is 0.030 so that I dare say that a result of 50/30 in our data is a statistically significant difference while 44/41 is not!

So you still do not believe in statistics? Well, all of the above was true, had our random variables X_k had equal distributions. Alas, in the real virtual PPM life the outcome of a game with Tactics A vs Tactics B depends on so many additional factors, that we may fairly assume that for each k the expectation \mathbb{E}X_k=p_k is different. A generalization of the central limit theorem still holds, but what we are estimating is the value of n^{-1}\sum_{k} p_k. This value depends on the everchanging distribution of player skills, energies, injuries, teams that play this tactics – countertactics pair. In short, it depends on the available sample pool.

As long as the parameter distribution in this sample pool is neat enough, our data will give a faithful estimate of the performance of Tactics A vs Tactics B for “average” teams. I hope I have convinced you and thus can spare more detailed formulations and mathematical proofs of the corresponding results and conclude that there is a very good chance that our tactics-countertactics tables actually “work” for an “average” team, quod erat demonstrandum by this article.


June 2, 2009

Wir sind Herbstmeister!

Filed under: GVE chronicles — glanvalleyeaglets @ 1:14 pm

Press release.

Glan Valley Eaglets have finished the first round of the first season on the top spot in the German division III.7, scoring 44 points in 19 games (total record 14-2[0-1-1]-3, goals 130:53). Five players can be found in the top 6 scorer list of the league. Captain Jonathan Schirmer leads the list with 29 (11+18) points, Gunar Ihle has 26 (12+14), Mark Umbeck – 24 (13+11), followed by the top defenders Arno Karsten (11+11) and Timon Schürrle (6+16) with 22 points each.

With overall team rating of 53.24 points the team has reached its highest spot ever in the World rankings of PPM. At the moment Glan Valley Eaglets are ranked number 175 in the PPM World! (The total number of PPM hockey teams is 26922 currently.)

The manager speaks: “Yes, it is nice to be a Herbstmeister, it is an important moment in the young history of our team. I want to congratulate the players, they have done a great amount of hard work during the last months and the team play has improved enormously. Yet I would be more happy if we played like this in the last rounds of the season to reach the play-offs and spare our best hockey for the last games of the season.”

Update on tactics

Filed under: PPM.statistics — glanvalleyeaglets @ 11:56 am

Half of the first regular PPM season is over, the games of the last 19-th round were played yesterday, so this seems to be a good time to update the tactical tables. I have added data from a few Slovak and Czech leagues; of course, the 21 top German leagues are still in the data pool. The total number of games included in these statistics is close to 3 countries times 21 leagues times 10 games times 19 rounds, which amounts to 11970 games. It is over ten thousand, mind you! The reader is encouraged to check the results as I am not a machine and as this would give him an idea about how it is to click through 10k game reports!

The reward is more data and more insight in the performance of less frequently applied tactics, such as Defensive versus Breaking (which is the smallest sample with 183 played games).

So, here we go:

D/SK/CZ Normal Defensive Offensive Counteratt Breaking Forecheck
Normal XX-XX-XX 315-69-167 481-151-545 418-129-404 233-80-430 519-154-446
Defensive 167-69-315 XX-XX-XX 178-48-109 122-43-95 82-25-76 132-51-119
Offensive 545-151-481 109-48-178 XX-XX-XX 238-66-215 141-59-153 398-87-140
Counteratt 404-129-418 95-43-122 215-66-238 XX-XX-XX 163-28-66 133-54-251
Breaking 430-80-233 76-25-82 153-59-141 66-28-163 XX-XX-XX 121-39-133
Forecheck 446-154-519 119-51-132 140-87-398 251-54-133 133-39-121 XX-XX-XX

The same table showing percentage of wins, overtimes and losses (row vs column):

D/SK/CZ Normal Defensive Offensive Counteratt Breaking Forecheck
Normal XX-XX-XX 57-13-30 41-13-46 44-14-43 31-11-58 46-14-40
Defensive 30-13-57 XX-XX-XX 53-14-33 47-17-37 45-14-42 44-17-39
Offensive 46-13-41 33-14-53 XX-XX-XX 46-13-41 40-17-43 64-14-22
Counteratt 42-14-44 37-17-47 41-13-46 XX-XX-XX 63-11-26 30-12-57
Breaking 58-11-31 42-14-45 43-17-40 26-11-63 XX-XX-XX 41-13-45
Forecheck 40-14-46 39-17-44 22-14-64 57-12-30 45-13-41 XX-XX-XX

It seams that the primary ring of tactics-countertactics looks as follows.

* Normal beats Defensive
* Defensive beats Offensive
* Offensive beats Forechecking
* Forechecking beats Counterattacks
* Counterattacks beats Breaking
* Breaking beats normal

Beautiful ring of countertactics, isn’t it? Reminds me of Ouroboros, the famous serpent that bites into its own tail 🙂

Let me quote the PPM Guide once more. It says,

“Finding the right style of play helps you achieve better results. Try finding the right style for your players. You can select from mutliple options. It is up to you, whether you choose to play offensively, defensively, counterattacks or you choose other option on this page. There are two things to remember. First: For every style of play there is an effective counter-play, which can give an advantage to the team that uses it. Second: Every team has a different composition of player attributes and therefore a different style of play is suitable for each team. Therefore you need to find the right style of play that suits your team. A different style of play is suitable for a team with a weak goalie and brilliant forwards and vice versa for a team with a goalie star.”

I firmly believe that the ring of counter-tactics, at least the dominant ring of first 19 rounds, has been identified in the current research project and related efforts. There is no guarantee that this ring won`t change with time (it only takes a small tweak in the game engine), and, of course, the tactics is just one facet of this complex game – still in some cases, choosing the right tactics can make the difference between three points and zero.

Concerning identification of the best-suited tactics for a team with given player skills distribution, this seems to be more difficult issue to investigate. One reason is that currently most teams are still pretty equal in skills. Furthermore, brute-force approaches involving gathering a huge mass of data and extracting some more or less valuable information are effectively prohibited by the makers of PPM, for the skills of players from other teams are only visible after scouting. What can I say, keep investigating this by yourself and stay tuned!

May 17, 2009

The first Moon

Filed under: GVE chronicles — glanvalleyeaglets @ 9:53 am

The slowness of one day per day has certainly changed my perception of the game flow. It`s a nice feeling to be up to date with the virtual events and to sense the pulse of the first season. In particular, the short-term tactical aspect gets much more meaning than it was thinkable in the four-times sped-up beta.

Among the reasons why I started this weblog was to try catching things that otherwise would be forgotten far too soon. This is the first article for the Glan Valley Eaglets chronicles.

Glan Valley Eaglets can now look back on the first four weeks of the first full PPM season. In terms of rate of development this is the most exciting time as much can be done with relatively small amount of ppm money in a short time period.

The league. I had chosen to start the full version in a high but not the highest possible league, so the holy Random chose to put the Eaglets in German league III.7.
Having lost three games out of four in the pre-season preparation period, the Eaglets started the regular season with four straight victories. The goalie Uli Nock who joined the team from 2nd level SA recorded his first two shootouts 3:0 and 7:0. The streak of victories was stopped by an accidental overtime loss (5:6) against Berlin Capitals, but the next three league games were won again. After the furious start we got a 5 days long crisis during which the Eaglets lost two friendlies and, what is more important, two league games. Nevertheless, the Eaglets face the second Moon while leading the league both by points and team rating, two points ahead of another team from Kaiserslautern.

In German National Cup Eaglets are the lucky seed no. 13. The first round was free, and the second opponent was a TeamNoname. In the third round Eaglets face the first real opponent, Red Devils Berlin from II.4. Although Eaglets have the home ice advantage and slightly higher reserves of seasonal energy, nevertheless it is a K.O. round, so wing it or leave it.

Libertadores Cup. So far the Eaglets have played 7 games and lead the group D with 16 points (5-1-1). A top-notch result indeed! All the games were extremely close, all but one ending with less than 2 goals difference.

Development. Angelo d’Arrigo Ice Forum was extended to offer places for 550 spectators, and from Monday on will also offer snacks and drinks in three sectors. The training facility is on Level 4, the same level of regeneration is under construction, we have the 3rd level of human resources department and are building the 5th level of sports academy. The 275.4 k from the general sponsor cannot possibly cover these costs, so compromises must be gone into.

The team had to sell the top forward Dario Noack with OR 160, Off 37, Sho 37 with high qualities. The transfer fee was 1.7 millions. A second top transfer was the great coach Kenny Hartung, with OR 28; this brought another 1.5 millions ppm money. Finally, there was essentially a swap – Mathew Greis left the team for Mojmír Vávra. The second legionar is the Latvian forward Vents Vīļums who is a good substitute player for that transfer fee (50k).

In a word, Eaglets have had an excellent start in the dangerous new world. We shall see what the future brings. The development of the players and facilities certainly is a race with the Red Queen. Remember, you gotta run as fast as you can to stay on the spot. If you wanna move forward, ya gotta run still faster. I believe there is a second important rule. While the long-term strategical development is very important, we are not ready to disappoint the fans by selling everything of value in the market.

Team rating 31.14, world ranking no. 1110, goaltending 13, defence 13, offence 14, shooting 15

May 14, 2009

The russian science

Filed under: PPM.statistics — glanvalleyeaglets @ 8:14 am

The West has always lagged behind the Russians in the scientific arena.” The certain irony lying behind this citation which I took from the homepage of David Wilcock lies in the fact that both I and Wilcock use some real scientific data from Russia to feed wild speculations about a universe 🙂

A group of scientists from the data mining field under the supervision of the leader of Black Pilots have ( here and here ) published the last and ultimate volume of data about the performance of tactics and countertactics in PPM hockey game.

This time I will play Prometheus and not the Eagle and bring these secret data to you dear reader who have stumbled across this weblog. The data are stored as follows. #{Games with regular victory for row tactics} – #{Games with Overtime} – #{Games with regular victory for column tactics}

RU/UKR Nor Def Off Cat Brk Fch
Normal XX-XX-XX 57-8-46 111-43-148 96-30-83 37-24-86 65-11-74
Defensive 46-8-57 XX-XX-XX 40-11-30 17-4-18 23-5-14 19-6-21
Offensive 148-43-111 30-11-40 XX-XX-XX 71-21-44 47-13-41 52-7-26
Ctrattack 83-30-96 18-4-17 44-21-71 XX-XX-XX 39-6-15 17-7-46
Break up 86-24-37 14-5-23 41-13-47 15-6-39 XX-XX-XX 15-2-28
Forechcng 74-11-65 21-6-19 26-7-52 46-7-17 28-8-15 XX-XX-XX

These data are from the Russian leagues I.1-III.16 and Ukrainian leagues I.1-II.4, including all Russian games of Days 1-8 and some incomplete input from later days 9 and 10. These data may be regarded as independent from mine and hence interesting.

May 10, 2009

How important is game importance?

Filed under: PPM.statistics — glanvalleyeaglets @ 6:54 pm

“I am hunting high and low…” or should I rather take the golden mean and go with normal? This is supposed to be a high-wire act, a matter of balance – to save the seasonal energy while not losing too many points due to low importance.The cost in terms of energy has been revealed. A league game with low (L) importance costs 0.08 points of seasonal energy, for normal (N) importance it is 0.4 and 2.0 for high (H). The cost in terms of performance is far less known. So let us let the statistics speak. German leagues I.1 thru III.16 again, games of TeamNonames excluded.

Day H-N H-L N-L
1 12-1-12 2-1-1 5-1-9
7 7-5-16 0-0-2 6-2-9
8 17-4-15 1-2-0 8-1-8
9 8-6-18 0-0-2 5-7-9
10 13-6-17 1-0-1 10-2-7
NC2 21-8-32 0-2-2 8-1-11
11 13-3-10 2-0-1 11-0-11
12 10-7-14 1-0-2 11-0-5
13 8-5-19 1-0-3 4-3-14
Total 109-45-153 8-5-14 68-17-83

Please take these stats with a grain of salt. I can think of many reasons why these data might be biased, such as:

– frequent usage of high importance leads to rapid energy decrease, which in turn leads to bad performance of High in this very study.
– good managers will seldomly use high importance in a league game.
– managers of teams performing above average in their league might go down to low importance for some games. If there is a bias towards stronger teams in the pool of teams playing Low importance, this will surely influence the stats in favour of Low importance.

This has a personal flavour, too 🙂 In the first 7 rounds my team played with Normal importance and did not lose a single game. When I switched to Low importance, we lost 2 league games out of 3! All low counterattacks vs normal breakup…

May 8, 2009

Raw data from the maze of tactics

Filed under: PPM.statistics — glanvalleyeaglets @ 8:04 am

Please don’t ask me about the purpose of this posting, it is just what it is. A small-scale study of the relative performance of different ppm hockey tactics.
Let me credit the manager Gibeor who inspired this study by his own project Тактика-контртактика.

The data were collected manually using the match protocols of games played in the first three German leagues I.1 III.16. The games with participation of TeamNoname were excluded from the statistics.

Notation: Day: match day of the league game (NC – National Cup), Nor: Normal tactics, Def: Defensive, Off: Offensive, Cat: Counterattacks, Break: Breaking up of play, Fch: Active forechecking. Since not all the names of tactics have been translated consistently (the meaning of Pressing is different from German to Russian version), here is a small dictionary.

English Normal Defensive Offensive Counterattacks Breaking up of play Active forechecking
Deutsch Normal Defensive Offensive Konter Pressing Aktives Forechecking
Latviešu Normāli No aizsardzības Uzbrūkošs Pretuzbrukumi Pretinieka spēles izjaukšana Aktīva atbloķēšana
Русский Средне Оборонительный Атакующий Контратаки Разрушение игры Активный прессинг

The data are stored in a self-explanatory way. For example, if you seek the performance statistics of Defensive tactics against Offensive tactics, you find the third table (against Offensive) and the third column (for Defensive). The entries are total number of wins – overtimes – losses; these are further sorted by the league game days (in separate rows). The bottom row is the sum over all games of the season.

Performance against opponents playing Normal

Day Def Off Cat Break Fch
1 3-0-3 15-2-10 4-3-4 12-1-5 11-6-16
2 0-3-3 9-1-9 6-2-6 15-3-8 12-2-11
3 4-2-7 14-0-5 4-5-6 21-2-10 8-3-10
4 2-1-5 14-3-10 10-0-5 17-2-7 11-7-10
5 3-1-4 8-1-14 10-0-5 12-3-7 7-3-14
6 2-1-4 4-2-11 5-0-2 7-4-7 5-4-6
7 1-0-1 4-4-12 6-1-3 10-3-9 6-2-12
8 2-2-5 5-3-12 3-1-8 9-3-4 10-5-4
9 2-0-5 5-2-5 5-2-7 15-4-5 5-2-8
10 3-2-7 6-5-5 7-0-3 12-4-3 12-3-10
NC2 3-0-5 12-1-9 7-3-5 13-7-10 12-4-15
11 0-3-2 9-2-5 3-3-4 14-1-6 3-1-5
12 2-0-3 13-1-4 6-0-5 9-2-6 12-2-9
Total 27-15-54 118-27-111 76-20-63 166-39-87 114-44-130

Performance against opponents playing Defensive

Day Nor Off Cat Break Fch
1 3-0-3 0-0-2 1-0-0 2-0-1
2 3-3-0 0-0-3 1-1-1 2-0-2 2-0-3
3 7-2-4 1-0-0 2-0-2 2-1-0 0-0-1
4 5-1-2 1-2-2 2-0-1 1-0-0 2-0-2
5 4-1-3 0-0-1 1-1-1 1-0-0
6 4-1-2 1-0-2 0-0-1 1-0-3 1-0-1
7 1-0-1 1-1-2 0-0-2 1-0-2
8 5-2-2 0-1-2 0-1-1 2-0-0 0-1-1
9 5-0-2 0-0-2 0-1-1 3-0-1
10 7-2-3 1-1-2 1-0-0 1-1-1
NC2 5-0-3 2-0-0 0-0-1 2-1-0 1-1-0
11 2-3-0 0-0-2 1-0-2 1-0-3 1-0-2
12 3-0-2 1-0-2 3-1-1 0-0-2
Total 54-15-27 8-5-21 7-3-13 16-4-10 15-3-17

Performance against opponents playing Offensive

Day Nor Def Cat Break Fch
1 10-2-15 2-0-0 0-0-4 4-3-0 0-2-3
2 9-1-9 3-0-0 2-1-1 2-1-0 1-1-5
3 5-0-14 0-0-1 1-1-3 4-0-2 2-2-2
4 10-3-14 2-2-1 0-0-2 3-0-3 2-1-0
5 14-1-8 1-0-1 5-1-6 1-1-2
6 11-2-4 2-0-1 0-1-1 1-2-2 4-0-4
7 12-4-4 2-1-1 2-1-3 5-0-2 1-0-2
8 12-3-5 2-1-0 1-1-0 2-2-3 2-0-1
9 5-2-5 2-0-0 1-1-1 5-0-3 3-1-2
10 5-5-6 2-1-1 3-0-1 2-1-2 1-0-3
NC2 9-1-12 0-0-2 4-3-0 1-1-2 2-0-1
11 5-2-9 2-0-0 2-0-1 2-1-5 1-1-3
12 4-1-13 2-0-1 4-2-2 1-2-3 0-1-7
Total 111-27-118 21-5-8 21-11-20 37-14-33 20-9-37

Performance against opponents playing Counterattacks

Day Nor Def Off Break Fch
1 4-3-4 4-0-0 0-0-2 3-1-1
2 6-2-6 1-1-1 1-1-2 0-0-6 1-0-0
3 6-5-4 2-0-2 3-1-1 1-0-1 1-1-1
4 5-0-10 1-0-2 2-0-0 0-0-2 2-0-2
5 5-0-10 1-0-0 1-0-1 1-0-0 2-1-1
6 2-0-5 1-0-0 1-1-0 3-0-4 2-0-1
7 3-1-6 2-0-0 3-1-2 1-0-3 2-0-1
8 8-1-3 1-1-0 0-1-1 2-1-2 4-1-0
9 7-2-5 1-1-0 1-1-1 1-1-2 2-1-1
10 3-0-7 0-0-1 1-0-3 2-0-1 1-0-0
NC2 5-3-7 1-0-0 0-3-4 0-0-1 2-0-2
11 4-3-3 2-0-1 1-0-2 1-0-3 2-0-1
12 5-0-6 2-2-4 0-0-1 2-0-1
Total 63-20-76 13-3-7 20-11-21 12-2-28 26-5-12

Performance against opponents playing Breaking up of play

Day Nor Def Off Cat Fch
1 5-1-12 0-0-1 0-3-4 2-0-0 1-0-2
2 8-3-15 2-0-2 0-1-2 6-0-0 1-0-0
3 10-2-21 0-1-2 2-0-4 1-0-1 2-1-1
4 7-2-17 0-0-1 3-0-3 2-0-0 3-2-1
5 7-3-12 1-1-1 6-1-5 0-0-1 4-0-1
6 7-4-7 3-0-1 2-2-1 4-0-3 4-1-0
7 9-3-10 2-0-5 3-0-1 4-0-3
8 4-3-9 0-0-2 3-2-2 2-1-2 3-0-2
9 5-4-15 3-0-5 2-1-1 2-0-4
10 3-4-12 2-1-2 1-0-2 1-1-3
NC2 10-7-13 0-1-2 2-1-1 1-0-0 1-0-3
11 6-1-14 3-0-1 5-1-2 3-0-1 2-0-4
12 6-2-9 1-3-3 3-2-1 1-0-0 2-0-3
Total 87-39-166 10-6-16 33-14-37 28-2-12 30-5-27

Performance against opponents playing Active Forechecking

Day Nor Def Off Cat Break
1 16-6-11 1-0-2 3-2-0 1-1-3 2-0-1
2 11-2-12 3-0-2 5-1-1 0-0-1 0-0-1
3 10-3-8 1-0-0 2-2-2 1-1-1 1-1-2
4 10-7-11 2-0-2 0-1-2 2-0-2 1-2-3
5 14-3-7 0-0-1 2-1-1 1-1-2 1-0-4
6 6-4-5 1-0-1 4-0-4 1-0-2 0-1-4
7 12-2-6 2-0-1 2-0-1 1-0-2 3-0-4
8 4-5-10 1-1-0 1-0-2 0-1-4 2-0-3
9 8-2-5 1-0-3 2-1-3 1-1-2 4-0-2
10 10-3-12 1-1-1 3-0-1 0-0-1 3-1-1
NC2 15-4-12 0-1-1 1-0-2 2-0-2 3-0-1
11 5-1-3 2-0-1 3-1-1 1-0-2 4-0-2
12 9-1-12 2-0-0 7-1-0 1-0-2 3-0-2
Total 130-44-114 17-3-15 35-10-20 12-5-26 27-5-30

Last update 17/5/9

The tables and this page might be updated or not updated on more or less regular basis depending on my mood, time schedule or any other objective or subjective reasons left to my own discretion.

Caution: If you stumble upon this entry and plan to apply this for your next match in PPM, be careful! The match result will depend not only on the tactics but also on the players’ skills, energy, lines, special lines, game importance and other tactical aspects. A team may be better suited to play one tactics than another. Finally, there is an allmighty god or goddess that we know by the name HOLY RANDOM.

There are situations where average data just don’t help. The average temperature of two sick persons may be 36.6 degrees centigrade even when one has 42 and the other 31.2, right?

May 7, 2009

We claim this space as our Eyrie

Filed under: Uncategorized — glanvalleyeaglets @ 12:29 pm


we are the Glan Valley Eaglets. It is true what they say, we only exist as numbers in a database which is a part of a hockey simulator, the powerplay manager (PPM), and yet we claim this space as ours! Actually it was our manager – he has such a big mouth that he just can’t shut up until they finally kick him out of the place. Any place. We just wanted to give him some space outside our locker room, so he can live out his writing compulsion without talking the pseudointellectual sh#t out of us. He is dangerous indeed, he can’t write or speak correctly either English, German or Russian, and yet he tries. Unfortunately. When facing him, you can expect to hear a mixture of any of these languages in any combination. Sometimes even Latvian, which happens to be his mother tongue. Other than that, he is our freakin’ manager and we are giving all we can so that his plan comes together on the ice.

Now that this is our eyrie, we can define our hunting fields. We can expect reports from PPM, both concerning us, the Glan Valley Eaglets and PPM as such. Perhaps an occasional bit of statistics gathered by our freak manager. Who knows what it might be good for!

« Newer Posts

Blog at