The [ppm] eyrie

January 19, 2010

Players

Filed under: powerplay manager hockey, PPM.miscellaneuous, Uncategorized — glanvalleyeaglets @ 3:21 pm

This is an article I always wanted to do – about the named bundle of attributes, qualities and statistics, about the silent knight of Powerplay Manager, about the goat, ass, horse or hypogrif that carries or brings down the hopes of the manager, the entity that gets nominated for the national team and without hesitation goes through fire and water, checks and fights, not afraid of any kind of fractures, inflammations, abdominal stretches and anginas when serving his Manager, who are you, my dear reader. Let me introduce Mr. Ice Hockey Player. Treat him well and he will thank you on the ice.

Your goal must be to train the player correctly, preferably so that he is the best possible player he could ever be at any time. I’ll leave the definition of the phrase “best player” as an exercise for the interested reader 🙂

The best service you can do to a Player is give him the right training for a certain position. The player will become your vision, so take care to have the right visions. It is perfectly all right if you want to turn a young center into a winger, if he has the quality-wise disposition for this. Scout your players, this will show you the way.

Read the Guide if you haven’t done so yet. Re-read it every now and then. The Guide was never written by the actual developers, so it is no wonder that it contains lots of bugs and omissions. Follow the Guide, but don’t trust it. Don’t trust me either, I’m much further from the developers than the authors of the Guide.

One of the most quoted and mysterious passages in the Guide is the following:

“Player with attributes 180 – 25 – 25 or 70 – 90 – 90 /where the first one is the primary main attribute and the last two are secondary main attributes/ is not as good for the given position as a player with attributes 120 – 30 – 50. Similarly a player with attributes 130 – 80 – 30 or 80 – 80 – 80 is not as good as a player with attributes 100 – 80 – 50.”

What in the world is it supposed to mean? Here are two of the most common misinterpretations of this verse:

* Guide states that 100-80-50 is the best distribution
* There is a built-in penalty for excessive secondary attributes, so 70-90-90 might be strictly worse than 70-70-70.

The Guide doesn’t state any of this. Instead, the Guide
* identifies a player with its primary and secondary attributes (three numbers) and
* states that a player P can be strictly better or worse than player Q for a given position.

Hold your horses, what does it mean “better”? My best guess is that the Guide compares players according to their contribution to the team and line strength rating (a.k.a. the pucks and the stars). In this sense one can introduce a number that measures the effectivity of a player for a certain position. I sometimes call it the “effective primary attribute” (EPA).

The guide confirms that EPA depends only on the primary and both secondary attributes. The possible dependence of this relation on the position remains obscure.

I will try to interpret what the Guide tells about the EPA, and will use some maths. If you want to skip this section, you are welcome to do so.

Let me denote the primary and secondary skills by A, B and C. We are looking for a non-negative function of three non-negative arguments with following properties:

1) Monotonicity (better attributes = better player): if A_1>A_2, B_1>B_2, C_1>C_2, then EPA(A_1,B_1,C_1)>EPA(A_2,B_2,C_2)
2) the A-skill monster punisher: as any one or two of A, B, C tend to infinity and the third remains fixed, EPA(A,B,C) remains bounded.

The simplest functions with the desired properties (punishing the weakest link) are involving the minimum operator:
EPA(A,B,C)= \mathrm{min} \{A, B/\beta, C/\gamma\},
where \beta, \gamma>0 are constants. Such function has a clear interpretation: the optimal ratio of skills is 1:\beta:\gamma.

The constants can be estimated by using the examples from the Guide. The strictest inequalities are:

1) (70-90-90) < (120-30-50) \implies   \frac{1}{\beta} > \frac{7}{3}
2) (130-80-30) < (100-80-50) \implies \frac{1}{\gamma} < \frac{10}{3}

I will add another one:
3) Since the first secondary skill cannot be less worth than the second secondary skill, we have \frac{1}{\beta}\leq \frac{1}{\gamma}.

(1-3) together imply: \frac{7}{3} < \frac{1}{\beta} \leq \frac{1}{\gamma} < \frac{10}{3},

So according to the Guide, the optimal distribution should be in the range (7-10):3:3.

At the beginning of the third season a group of Latvian managers carried out an experiment to find out the optimal primary-to-secondary skill ratio for goalkeepers. They nominated only one goalie for the first game of the league, and then collected the attributes, chemistry and experience in a table together with the rating for goaltending. The data were surprisingly consistent and clearly demonstrated that the ratio 1:0.5:0.5 = 2:1:1 gives the best results.

It also supported the old rumors about the influence of Chemistry and Experience. This law of thumb states that “100 points of chemistry give +20% to the attributes and each 100 points of experience give additional +20% to the attributes.”

So this might be the key to high rating of team strength. Have we come closer to answers to the question – what is the best training for my Mr. Player?

Not necessarily.

* The best Defender will not help his team much if he spends his life in the cooler. A good way to reduce his time on the penalty bench is – train up his technique to match the aggressiveness. Open question: should the Tec:Agg ratio be 1:1, 9:10, or perhaps even 2:1?

* The shooting attribute is independent from the primary/secondary bundle. Open question: which ratio to the primary and secondary skills is the best for the different positions?

* Passing, Technique and Aggressiveness: is there a use for higher values of these attributes than half of the primary skill?

* The “alien primary skills”: does the center need defense and if so, how much?

* Special players: perhaps it is wise to build several types of players – the offensive specialists for PP, defensive masters for PK, mix up good passers and good shooters to increase the productivity of a line? If so, then different attribute ratios should be followed for different players.

It is up to the manager.

Now back to the quality of a player. Yes, I mean it, THE quality.

Suppose you want to train your Player in k skills with the ratio R_1:R_2: \dots :R_k. Let the corresponding qualities of the attributes be Q_1, Q_2, \dots, Q_k and the daily progress of the attributes at the current stage of development is correspondingly P_1,P_2, \dots, P_k.

Then the effective quality of the player for that ratio is given by the weighted harmonic mean

\frac{R_1 + R_2 + \dots + R_k}{\frac{R_1}{Q_1} + \frac{R_2}{Q_2} + \dots + \frac{R_k}{Q_k}},

and the average time in days for the player to increase his overall rating by one point with the current facilities and staff is the weighted mean

\frac{\frac{R_1}{P_1} + \frac{R_2}{P_2} + \dots + \frac{R_k}{P_k}}{R_1 + R_2 + \dots + R_k}.

Along with the age and career longevity, the effective quality is the only parameter that determines the future of the player and should be the only number to look at when evaluating a future prospect!

(A side note. There is a weird misconception traveling around, which is called the average of important qualities. This has no “physical” interpretation and anybody using it should be sued for crimes against maths. Qualities 60-60-60 are MUCH better than 85-85-10!)

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November 6, 2009

Advanced PPM-o-metrics

Filed under: powerplay manager hockey, PPM.statistics — glanvalleyeaglets @ 3:43 pm

Howdy everyone,

I haven’t posted for a while, you should appreciate the reasons – the daily job, the autumn blues, Dinamo Riga underperforming in the second KHL season and, most importantly, the lack of fresh ideas and results to present.

There are many discussions out there about the use and futility of counter-tactics, inconsistency of the game engines, about theories how to build player attributes to suit the demands of certain tactics. I don’t “know” the answers, all I can provide is evidence based on evaluation of over 200 000 games played in the second season of powerplay manager hockey. My opinions and interpretations may be wrong, cruical aspects of the game might be missing in my analysis and you can always believe your intutition outperforms any analysis, or you might just not believe the existence of a “game matrix” when facing the evident and omnipresent random factor making you win a game with 10:0 and losing the next one by 0:10 all settings being equal. Nevertheless I think I have a point to make and this blog is the perfect vehicle for doing so.

Let me keep this stupid and simple – apart from the aesthetics, ice hockey is all about scoring goals and preventing goals. In order to score goals, you have to shoot on the net and shoot with a certain quality. Everybody knows, what the average number of shots per game means. The quality of the shots is measured by the shot efficiency, which I’ll define as the inverse of “shots per goal” (the number of shots needed to score 1 goal). 5 goals with 40 shots gives an efficiency of 5/40 = .125 = 12.5%.

Take the number of shots per game and multiply it by the shot efficiency and you’ll recover the number of goals scored. Learn how to control the two factors and you’ll understand the game. What are the important factors and variables? How big or small is the random factor and what does it depend on?

The best variables available for analysing team strength are the “stars” shown in the profile page of a team. Let us suppose our team has a goaltending at 29 (notation: G+=29), defence at 26 (D+=26), offence at 25 (O+=25) and shooting at 23 (S+=23), and our opponent has goalkeeping G-=28, defence D-=19, offence O-=24 and shooting S-=17. In order to compare our offence to opponent’s defence, we introduce the offence-defence quotient O+/D-, which in this case would be 25/19=1.32. A good measure of comparison between our shooting quality and opponent’s goaltending quality is the shooting-goaltending quotient S+/G- given by 23/28=0.82. It turns out that these along with the corresponding quotients from opponent’s point of view (O-/D+ and S-/G+) are very important indicators for the chances of the teams.

Let us look at the graph in Figure 1. It shows the average number of shots per game versus the offence-defence quotient O+/D-. The blue line is computed from the data of all teams, the green line – from teams playing the right countertactics and the red line – from teams playing against the countertactics.

Avg shots per game vs O+/D-

Fig. 1. Average shots per game versus the O+/D- quotient for all teams (blue), teams playing countertactics (green) and teams playing against countertactics (red)

Evidently this is a “master curve” of the game. We see how the counter-tactics work (improving the shot differential), we see that teams with a weak offence perform few shots against a team with a strong defence, and we see that there is a saturation effect – the average number of shots per game stays below 35.

The lines show an almost cosmic order, but there is the back-side – the mighty random. To estimate its influence, let me introduce the variance \sigma=\sqrt{N^{-1}\sum_{i=1}^N (x_i-\bar{x})^2}. It is a moment of probability distribution function, but very roughly saying, in most games the deviation will be considerably smaller than \sigma.

O+/D- Shots p G Variance \sigma
30-40 16.5 3.28
50-60 20.8 7.17
80-90 27.4 12.8
120-130 31.0 15.8
150-160 32.3 17.1
170-180 32.9 17.4
200+ 33.2 17.5

What this shows is that a weak offensive line will not likely create many shots against a much stronger defence (low variance at low O+/D- levels), but the stronger team may have a good or bad shooting day 🙂 (By the way, the rule of three sigmas doesn’t apply here as the number of shots is not a normal random variable.) (Should provide a graph to illustrate this in the future.)

So we have learned that the number of shots on the net depends on the offence-defence quotient. If one keeps the O+/D- quotient constant and increases the strength of the own defence, the number of shots slightly increases. This probably has to do with the fact that the better defence creates more offensive situations and the defenders themselves are more likely to take a shot or two. We also have seen the huge variance, so don’t come to me and complain that your team managed only 20 shots whereas it was suppose to generate at least 30. It will all even out in the long run 🙂

Do different tactics have different number of shots versus O+/D- quotient curves? If this was true, then there might be a tactic suited for stopping better offensive lines, or a tactic that generates more shots on average, but is it so? Is there a secret key to success hidden in the sea of data?

Alas, see Figure 2.

ShotsTac

Figure 2. Shots per game vs O+/D- for various tactics (excluding counter-tactics situations). (Sorry, there are three 'extra' zeroes on the y axis, cross them out when reading the graph)

This study suggests that the answer is negative. Within the margin of errors the lines coincide. (It has to be remarked that towards the extreme ends of the graph the error margin increases considerably due to significanltly smaller amount of available data.) Once more, for underlining and pinning on the walls: no tactics is better suited to a certain team, every one will perform the same way in the long run. Or, more modestly, I could not confirm the opposite to be true although the Universe knows I tried 🙂

Let us now move to the second parameter – shot efficiency alias scoring percentage. Figure 3 below shows the mastercurve “shot efficiency vs the shooting-goaltending quotient S+/G-” for all teams (blue line), teams playing counter-tactics (red line) and teams playing against countertactics (red line).

EfF_CT

Fig. 3. Shot efficiency vs S+/G- for all teams (blue), teams playing countertactics (green) and teams playing against countertactics (red)

This chart is worth a thousand words. Not only does it confirm that the relation between shooting skill and opponent’s goaltending skill is the most important factor determining the scoring percentage, but it also shows an almost linear dependence of shot percentage on S+/G-. One more thing we can read off the curves is that counter-tactics do not influence the shot percentage.

To estimate the magnitude of random in scoring efficiency (including the famous goalie’s good/bad night effect), let us look at the variances.

S+/G- Shot effic. % Variance \sigma
30-40 5.9 5.84
50-60 10.2 6.09
80-90 13.7 7.03
120-130 18.4 7.91
150-160 21.9 8.92
170-180 24.2 9.42
200+ 29.1 10.6

As we see, the variance grows slower than the scoring efficiency, hence the bigger your S+/G- advantage, the less likely gets the chance for the great upset. It is difficult enough for an average goalie to stop a much stronger offence. However, and I want to emphesize this, the stronger the goalie, the larger the relative fluctuations in his game!

However, S+/G- is not the only one parameter that influences the scoring percentage. You don’t have to train the shooting skill exclusively to improve your shot efficiency. This curve happens to shift considerably with changing offence-defence O+/D- quotient.

EfF_OD

Fig. 4. Shot efficiency versus shooting-goaltending ratio (S+/G-) for various offence-defence ratios (O+/D-)

This is illustrated in Figure 4.

Again, no words are required to explain the results – except that I’ve found out that the shot efficiency is more sensitive with respect to shooting-goaltending ratio S+/G- than it is to offence-defence ratio O+/D-. The bottom line is – you gotta train both offence and shooting for the best performance 🙂

The final picture for today studies the curves for different tactics played.

GKTac

Fig. 5. Goaltending efficiency versus goaltending-shooting ratio (G+/S-) for the different tactics.

Figure 5 shows the opposite quantities to Figure 4, namely, goalkeeping efficiency versus goalkeeping-shooting ratio G+/S-. However, it is basically the same thing as goalkeeping efficiency is just 100% minus shot efficiency and G+/S- is just reversed S-/G+, so basically it is the same thing (but the tactics are chosen by the defensive team). The thing to take home from here is the fact that the curves coincide within the margin of error. No optimal tactics for a great goalie or for a team with great shooting, it is all the same 🙂

I’d like to conclude that the following passage in the Guide is henceforth a busted myth:

“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. “

Hopefully they’ll implement it some day 🙂

Right, folks, that’s it for today. Good luck for the upcoming playoffs everyone! (And thanks for your interest, I never expected to get tens of thousands of hits in the first half-year of this blog!)

September 17, 2009

The key factor

Filed under: powerplay manager hockey, PPM.statistics — glanvalleyeaglets @ 12:08 pm

Let us put the tactics and game importance aside for a while and ask: what is the most important factor that decides the outcome of a game? Today we’ll talk team strength. Whether we like it or not, as much as we sometimes wish an underdog to win, the stronger teams usually do better and prevail in the long run. The ice-hockey simulation in powerplay manager is no exception.

One could write monographs about what makes a great team great. Luckily, it is easier in PPM. Each team has a profile page where you can find the estimate of team strength based on the lineup used in the previous official game. These are the ominous “stars”, the four integers indicating the levels of Goaltending, Defence, Offence and Shooting. There is a fifth one that shows the total team strength, but it is just the arithmetic mean of the former four. So let us look at the four numbers as a measure of team strength.

There is a long ongoing discussion about “what do you mean by saying that your team was much stronger”. With the scale going up to 200, it doesn’t sound like a big difference between 15 and 20, it is a basic beginners level. In the same time the difference between 15 and 20 is 25% down or 33% up, and this is no peanuts anymore. We can see whether our team is better or worse in terms of the stars, but how does it affect the chances of winning the bloody game? Be the first to know and keep reading this great feature article in the [ppm] eyrie. We bring to you the whole story as it unfolds! Blah, blah, blah!

A typical ppm ice hockey team in the middle of second season might have Goaltending GT rated at 16, Defence DF=16, Offence OF=15 and Shooting SH=14. My Eaglets have (23, 23, 22, 19); Radowan’s Enterprise is currently rated at (30,25,28,17), the best Latvian team Pardaugavas Lauvas impresses with (31,29,23,22).

We’ll do the simplest thing out there and just sum the four indicators (GT + DF + OF + SH) of both teams and compute the difference, and see how the teams perform against each other in dependence on this difference.

The results can be summarized in a table. The first column shows the difference, then the percentage of wins (in regular time), overtimes and losses of the stronger team and finally the number of games used to calculate the “odds”.

Diff W % OT % L % N
1-2 47.3 15.0 37.5 7588
3-4 51.8 14.5 33.5 7176
5-6 56.8 14.2 28.9 6153
7-8 62.0 13.4 24.4 5363
9-10 68.0 11.9 20.0 4609
11-12 72.3 11.0 16.6 3906
13-14 77.5 10.1 12.3 3412
15-16 81.7 8.0 10.2 2865
17-18 84.4 8.0 7.4 2298
19-20 88.6 6.9 4.4 1859
21-22 90.2 5.7 3.9 1512
23-24 92.9 4.2 2.7 1199
25-26 94.6 2.4 2.9 892
27-28 95.8 1.6 2.5 718
29-30 96.8 2.2 0.9 540
31+ 99.8 0.1 0.0 5199

The trend is visible, isn’t it? The advantage of some 6-7 team strength points weighs approximately as much as the correct counter-tactics. I hope to get to the corresponding effect of game importance in a future article.

How does this info add to the understanding of the game? Let’s speculate and assume that I am to throw my 87 points against Liepinsh’s 105. Under normal conditions I’m at -18 meaning that my chances of winning the game are somewhere around 7.4%. Can I influence my odds? Sure, I can choose the game importance and tactics. With the right counter-tactics, the odds would shift in my favor, perhaps the shift is worth as much as 6 points making the gap approx. -12 points wide. Why, according to this arithmetics, the chance of winning just sky-rocketed to 16%! If I am lucky and my team actually plays that tactics well and if I use higher game importance, my chances might improve even further. (Of course, the life is never as simple as that) 🙂

Just want to make a final remark. Perhaps I am looking at the wrong thing. The quotient of team strength indicators might be more important than the difference. Say, is the difference between 110 and 100 points “ten points” or “ten percent” wide? Is it as good as 20 vs 10 (difference) or as good as 11 vs 10 (quotient)? Or is it something in between? I don’t know the answer, but some day…

September 16, 2009

Tactics and home ice advantage

Filed under: powerplay manager hockey, PPM.statistics — glanvalleyeaglets @ 10:41 am

In the first season I was first and foremost interested in finding out the basic relations of the tactics and countertactics, so I only recorded the tactics and the score of the match. As the spread in the teams’ strength increases, the grand total tables make less sense, so I decided to create a more serious databasis for the second season. So for each game under consideration I store the tactics, game importance, team strength estimates (the “stars” for goaltending, defensive, offensive and shooting), shots, penalties and the final score. I find this is a much better tool for studying how this game works!

I hope this is the first post in a series of articles. Today I want to address a simple question: is there a home ice advantage in regular season games and what does it have to do with the tactics?

Let us start with the grand total table using all the games I’ve gone through. All are from the regular tournament of the second season, match days 1-16 (games with participation of inactive teams were excluded, but it was done with care in order not to lose games like this one 🙂 ). In total 48,770 games of powerplay manager ice-hockey. For those interested: the most popular pairing of tactics is Normal vs Normal with 5083 games; the most exotic is Defensive (home team) vs Breaking up (road team) with 416 games. Here the rows represent tactics of the home team, the columns – the tactics of the road team. The numbers are percentage of home wins, games with overtime and away wins.

Normal Offensive Defensive Counteratt Breaking Forecheck
Normal 48.1-11.4-40.3 42.3-11.8-45.8 63.1-10.4-26.4 47.1-13.5-39.2 37.8-10.1-52.0 45.8-10.8-43.2
Offensive 54.4-11.5-34.0 48.9-12.1-38.8 37.5-13.0-49.3 48.0-13.3-38.5 52.8-10.3-36.8 62.7-12.0-25.1
Defensive 37.8-12.5-49.6 59.6-12.4-27.9 48.0-12.3-39.6 51.0-11.6-37.2 47.8-11.7-40.3 43.6-13.2-43.0
Counteratt 49.3-12.0-38.6 48.1-12.6-39.2 47.6-11.3-40.9 48.0-13.2-38.6 58.1-11.8-30.0 37.0-9.0-53.8
Breaking 61.9-8.9-29.0 46.7-12.5-40.6 42.3-12.6-44.9 34.5-12.5-52.8 45.0-11.8-43.1 45.3-9.8-44.8
Forecheck 51.4-11.6-36.8 35.0-12.1-52.7 44.5-11.2-44.2 61.1-12.8-26.0 51.1-12.2-36.6 47.8-13.0-39.1

The home teams win approximately 48% of the games and lose some 40%, so there is a measurable home ice advantage in this game.

The average team in this study has goaltending rated at 16.1 stars, defense 16.0, offense 15.4 and shooting 14.1. No wonder that, for instance, offensive tactics overall does slightly better than defensive tactics (it is worth paying more attention to the weakest part of the team). For offensive teams, the opposite is the case!

On the top of this we clearly see the famous ring of countertactics in action!

Of course, games of equally rated teams are more interesting to us. Taking the sum of goaltending, defense, offense and shooting “stars” as a measure of team strength and filtering out all games where the difference exceeds 5, I was left with mere 18,508 games. Here is the resulting table:

Normal Offensive Defensive Counteratt Breaking Forecheck
Normal 49.5-14.5-35.9 46.8-15.7-37.4 65.9-12.0-22.0 50.4-15.4-34.1 38.7-12.0-49.1 47.5-12.9-39.5
Offensive 48.8-15.2-35.8 46.7-16.2-37.0 36.1-17.6-46.2 48.6-16.2-35.1 51.9-13.7-34.2 60.6-15.1-24.1
Defensive 34.5-16.5-48.8 63.3-14.3-22.2 45.9-14.2-39.8 45.8-16.2-37.9 49.4-11.7-38.8 43.4-17.3-39.1
Counteratt 51.4-13.7-34.8 52.0-15.2-32.7 48.2-14.5-37.1 47.7-16.5-35.7 60.0-12.8-27.2 36.3-12.1-51.5
Breaking 62.0-11.5-26.4 50.1-13.5-36.2 45.9-13.1-40.9 31.5-16.3-52.1 45.6-12.8-41.5 49.5-11.6-38.8
Forecheck 52.0-15.0-32.8 37.4-16.7-45.7 43.4-15.6-40.9 63.2-15.8-20.8 49.3-13.8-36.7 50.7-16.2-32.9

We see that in this case the draw margin is way bigger and that it is harder for the visiting team to win the game; home wins ~ 48%, away wins ~37% of the “equal” games.

I must stress that these tables are not there to suggest that “some tactics are better than others”. It is true that the tactics should suit your team and tactics that one team uses with great success may miserably fail for another team with different skill distribution.

So we have seen the importance of home ice in games of the regular season. It is claimed that there is no such thing in cup games or friendlies. In the next article I plan to go deeper into the overall team strength – performance relation. It will turn out that the team strength may be more important than the tactics 🙂

August 6, 2009

Aaaaargh, the economics!

Filed under: PPM.miscellaneuous, Uncategorized — glanvalleyeaglets @ 9:59 am

Let me start with a short story, an urban legend. Once upon the time there was a poor young lad who met a millionaire and asked him the obvious question – how he made his fortune? The old millionaire looked at the sky, smiled and said, “Yeah, well, my son, I still remember the times of the great Depression. I was down to my last nickel, but I never thought of giving in. Instead, I invested my last nickel in an apple. I washed and polished that apple and at the end of the day I sold it for 10 cents.

The very next morning I invested the 10 cents again and purchased two apples. I washed and polished them and sold for 20 cents. I kept going like that for several months and eventually made over ten dollars.

Then my old aunt died and left a fortune of 44 millions, and since then I have been a rich man.”

What bothers me in Powerplay Manager is the new sponsorship deals for the second season. No question, the contracts should increase with time, I am just a little bit worried about the rate of the growth. Contracts suddenly explode, with increments of something like 1,000% and more (approx. 2,000 % for the winners of I.1). I expect pretty dramatic effects on the game. One of the worst fears is that trading (and I mean rough trading, where you buy players with high A qualities, train a single skill and sell the poor guy who will never become a great player) might generate so high incomes that traders might dominate the whole game in a few seasons. I hope I’m wrong since this is a strategy I don’t approve of.

Another observation of mine is that teams from higher leagues usually get the better contracts. A difference of a single league can outweigh as much as 20-30 rating points. Sure, a good thing that motivates the promotion ASAP. There is nothing worse than grand contracts for teams that deliberately avoid promotion to boost the rating (weaker league!). On the other hand, the first season could be an exception with minimal financial differences between the leagues. Fine, I can live with that.

Right, now we have a few days to arrange with the new circumstances. I guess the trick is to focus on the own team – to build it, to choose the tactics and try to get the best results. Frustration usually comes from comparing with other teams.

Long live the new contracts! Prost!

August 3, 2009

Golden friendlies

Filed under: GVE chronicles — glanvalleyeaglets @ 10:13 am

Why do folks play friendly games? There are many reasons. You get some money from selling tickets, your players gain experience and team chemistry. If you win, you also get rating points. On another line, friendlies is an ideal terrain for trying out different lineups and tactical schemes. I can see no downsides for playing friendlies – unless you want to play on high importance, that is.

For the sake of fun friendly tournaments were invented. While the German speaking ppm community is still on its way to arrange the first friendly cup, the Latvians can prouldy look back on a long history of running such tournaments in the Beta phase of the game. My Eaglets had taken part in the first two seasons of the Challenge Cup and later changed to the oldest Latvian ppm cup – the Libertadores Cup.

The cup started with 28 teams (27 of them representing Latvia). In the group stage we had 4 groups, each with 7 teams playing two rounds against each other. This is the final standings in group D after 12 games. To put things in the right perspective, the Eaglets played all games on low importance. (There were some problems with arranging some of the games and because of this two teams were placed back in the final standings).

1. Glan Valley Eaglets – 31, 45:27
2. Lion Team – 22, 50:40
3. Etalons – 17 – 43:44
4. Apbedīšanas birojs 0.7 – 9, 35:46
5. Dullīši – 8, 35:48
6. Arsenich TM – 17, 31:40
7. Whitetigers – 11, 28:43

Three best teams from each group and 4 best of the rest qualified for the round of last 16. Our first opponent was
HK Valmet. Already the first game showed that there is no use of low importance in play-offs. Luckily, the Eaglets managed to win both of the remaining games. Here are the the results of the 1/8 final round:

HC KronosAndels 1:2 (0:5, 5:1, 2:4)
Glan Valley EagletsHK Valmet 2:1 (2:3, 6:4,5:4)
Latvian Tigersmildronats 1:2 (6:3, 2:7, 3:4)
kāmīšiJust do it!!! 2:0 (5:3, 10:6)
Rēzeknes dināmoEtalons 2:1 (2:3, 5:3, 3:2)
Aizkraukles MežoņiHC passatwind 1:2 (1:6, 5:4, 2:4)
RTM TigersRiga Mad Dogs 2:1 (6:2, 2:3 OT, 2:1)
Lion Teamvilkspiži 2:0 (5:0, 5:2)

In the quarterfinals, the Eaglets faced noone less than the team that would later win the first Latvian National Cup, the great RTM Tigers! Against all odds, the Eaglets did not lose this match. Here is what happened:

Andels – Lion Team 0:2 (1:2, 4:6)
Glan Valley Eaglets – RTM Tigers 2:0 (4:2, 6:5OT)
Mildronats – HC passatwind 1:2 (5:3, 1:4, 5:6SO)
Kāmīši – Rēzeknes dināmo 1:2 (2:1, 1:5, 1:3)

Who would stop us now? Lion Team hadn’t lost a single game in the playoffs, until… Here is what happened in the semi-finals:

HC Passatwind – Reezeknes dinaamo 2:0 (6:2, 6:3)
Glan Valley Eaglets – Lion Team 2:0 (3:1, 9:3)

The last part of the impossible mission was sailing vs HC passatwind and it ended with the first, albeit inofficial, title for my Glan Valley Eaglets. Just for the record:

HC Passatwind – Glan Valley Eaglets 0:2 (1:4, 1:6).

Here is how the winners of the first Libertadores Cup of the (pre-)final PPM hockey version played in the last game of the cup:

Glan Valley Eaglets: Florin (Nock); Zellweger-Plate, Ihle-Schirmer(C)-Grundmann; Emmerich-Buhs, Nikolaus-Umbeck-Jacobsen; Brinkies-Karsten, Doerr-Vavra-Clasen; Spaeth-Pestalozzi; Schellbach-Mosler-Niederwipper.

Goals: 0:1 Ihle (Zellweger); 0:2 Emmerich (Buhs); 0:3 Ihle (Schirmer, Zellweger); 1:3 Spulbergs(Vaivods); 1:4 Schirmer; 1:5 Schirmer; 1:6 Nikolaus (Buhs).

And so the Eaglets qualified for the inofficial Cup of Latvian Cup Winners along with other great Champions of the first season. The stellar field of participants:

Winner of the Latvian National Cup: RTM Tigers
Winner of the Libertadores Cup: Glan Valley Eaglets
Winner of the Challenge Cup: HK DINAMO RIiGA
Winner of the LHC (Latvian Hockey Cup): HK Murggs.

The round robin tournament was played in three days – an overtime or shootout victory bringing two points.

On the first day Eaglets faced Dinamo and lost, so Murggs and Dinamo took the lead:
RTM Tigers – HK Murggs 1:4
Glan Valley Eaglets – HK DINAMO RIiGA 3:5

The next round made the things more complicated – both winners of the first day lost their games…
RTM Tigers – HK DINAMO RIiGA 5:4 OT
HK Murggs – Glan Valley Eaglets 3:4

Now Dinamo were leading with four points, but the last round came with another surprises:
HK DINAMO RIiGA – HK Murggs 1:5
RTM Tigers – Glan Valley Eaglets 3:4

Now two teams were in front with 6 points, but Eaglets had won the game between the tied teams, so the winner is…
1. Glan Valley Eaglets 6
2. HK Murggs 6
3. HK DINAMO RIiGA 4
4. RTM Tigers 2

This was our lineup for the deciding game:

HK Murggs vs Glan Valley Eaglets 3:4 (0:1, 1:1, 2:2)

Eaglets: Florin (Nock); Zellweger-Plate, Ihle-Schirmer(C)-Grundmann; Schürrle-Buhs, Vīļums-Umbeck-Jacobsen; Brinkies-Karsten, Doerr-Vavra-Clasen; Schiebler-Pestalozzi, Schellbach-Mosler-Ortel.

Goals: 0:1 Schiebler, 1:1 Dreilings (Lauva), 1:2 Zellweger (Grundmann, Plate), 1:3 Clasen (Vávra), 2:3 Pūpols (Bērziņš), 2:4 Buhs (Umbeck, Vīļums), 3:4 Lauva (Barovskis, Grundulis)

So our Captain Jonathan Schirmer has had a good training in weightlifting, the two cups can be pretty heavy 🙂 It is hard to put this title in the right perspective – of course, it was just friendlies and it is nothing like winning a National Cup (sweet dreams…). On the other hand, all the Latvian teams participated in the National Cup and many of the greatest ones were represented in the cups, so it is hard to digest, how much luck we have had in this season, the season of golden friendlies 🙂 No time for Champagne, promotion games lie ahead!

July 16, 2009

Tactics Check XL

Filed under: PPM.statistics — glanvalleyeaglets @ 8:31 am

Recently we have witnessed quite a few controversal discussions about the use and abuse of tactical tables. In the course of the discussions, the focus has shifted from “how do I use tactics to beat my opponent” to “how do I do my best to outshoot him”?

The shot differential is influenced by many factors. First there are the offensive and defensive skills of the guys on the virtual ice – all other factors being equal, the better team should outshoot the opponents. (Just for the record, in many cases it is far from being clear, which team is better.) Further important factors are game importance and seasonal energy – this has been documented in another article. The chemistry of the lines is a big deal too, and as the experience of the best players increases it might also play a major role in the coming seasons. There are more factors like home ice advantage etc. Now the tactics are supposed to act on this complex background.

Believe me, I would like to make scientifically correct statements about the tactics-countertactics relations, but I’m not in position of doing so. Technically speaking, we are dealing with multidimensional data with in part large uncertainties. We are about to ignore all the various “dimensions” – save the tactics 🙂 – and try to extract information on distribution of victories, goals, shots and penalty minutes. In theory, such model reduction requires a careful preparation and pre-conditioning of the incoming data. The main point is that if we want to ignore a variable, we ought make sure that the data are not biased by that variable…

The sociologists are dealing with similar problems when conducting some polls – the point is to choose the sample pool so that one can extrapolate the data from 1000 people to many millions and obtain realistic results (contrary to my intuition, this is indeed possible). In PPM we would require a range of teams with parameter distributions characteristic of the whole PPM, and getting this is hardly possible without breaking the rules (e.g., creating a thousand of teams for tests).

In the present study we go another way and sample the huge number of games by… a very large number of games! There is a certain doubt whether the skill distributions of teams preferring a given tactics is close enough to the skill distributions of all teams of PPM. All I can say is – nevermind.

To be sure we are after something that is real, let me cite one of the leading guys in PPM from the English forum.


vlady 10.07.2009 15:59:10
THE OFFICIAL STATEMENT OF THE DEVELOPMENT TEAM

You have been waiting for this for a long time and here it is. The number of shots is determined by the overall strength of the team compared to opponent and by the the tactic that you use. We will not disclose the details but these are the two main components that determine the number of shots on goal. It means that if you have a better team and if you have chosen the right tactic, you will most likely outshoot your opponent.

This part of the game engine is planned to be improved in the future though. We plan to take into account several other factors for you to ponder about.

Enjoy the game and don’t stress too much! Chill out people!

Ok, enough of text, let us turn to the results. Again, the data are given in the format Row vs Column. First line shows the percentage of wins, OTs and losses, S shows average shots per game, P penalty in minutes and N is the number of samples (i.e., games).

Normal Defensive Offensive Counteratt Breaking Forecheck
Normal 58.5-10.2-31.2
G: 4.41 – 3.17
S: 30.1 – 22.9
P: 2.98 – 4.00
N: 1855
42.6-11.7-45.5
G: 3.83 – 3.87
S: 27.1 – 27.2
P: 3.46 – 3.55
N: 3866
48.1-11.8-39.9
G: 4.04 – 3.61
S: 27.8 – 26.5
P: 3.31 – 3.68
N: 2548
36.1-11.0-52.7
G: 3.47 – 3.99
S: 23.9 – 29.3
P: 3.79 – 3.20
N: 1902
47.9-11.3-40.6
G: 4.12 – 3.63
S: 28.0 – 26.7
P: 3.29 – 3.73
N: 2816
Defensive 31.2-10.2-58.5
G: 3.17 – 4.41
S: 22.9 – 30.1
P: 4.00 – 2.98
N: 1855
53.7-11.2-35.0
G: 4.13 – 3.39
S: 29.7 – 23.8
P: 3.08 – 3.88
N: 1335
42.0-12.9-45.0
G: 3.77 – 3.88
S: 27.4 – 27.1
P: 3.39 – 3.75
N: 844
44.8-9.9-45.1
G: 3.80 – 3.83
S: 27.5 – 26.9
P: 3.40 – 3.59
N: 591
45.1-11.6-43.1
G: 3.93 – 3.80
S: 27.3 – 27.1
P: 3.54 – 3.51
N: 915
Offensive 45.5-11.7-42.6
G: 3.87 – 3.83
S: 27.2 – 27.1
P: 3.55 – 3.46
N: 3866
35.0-11.2-53.7
G: 3.39 – 4.13
S: 23.8 – 29.7
P: 3.88 – 3.08
N: 1335
47.0-10.9-42.0
G: 3.92 – 3.74
S: 27.4 – 26.8
P: 3.47 – 3.55
N: 1722
45.7-9.8-44.3
G: 3.90 – 3.69
S: 27.4 – 26.2
P: 3.25 – 3.63
N: 1116
60.7-11.3-27.8
G: 4.62 – 3.06
S: 30.4 – 22.1
P: 2.89 – 4.11
N: 1801
Counteratt 39.9-11.8-48.1
G: 3.61 – 4.04
S: 26.5 – 27.8
P: 3.68 – 3.31
N: 2548
45.0-12.9-42.0
G: 3.88 – 3.77
S: 27.1 – 27.4
P: 3.75 – 3.39
N: 844
42.0-10.9-47.0
G: 3.74 – 3.92
S: 26.8 – 27.4
P: 3.55 – 3.47
N: 1722
56.6-13.0-30.3
G: 4.27 – 3.14
S: 29.9 – 23.0
P: 2.98 – 3.94
N: 738
35.5-9.2-55.2
G: 3.32 – 4.24
S: 23.4 – 29.8
P: 3.77 – 3.23
N: 1343
Breaking 52.7-11.0-36.1
G: 3.99 – 3.47
S: 29.3 – 23.9
P: 3.20 – 3.79
N: 1902
45.1-9.9-44.8
G: 3.83 – 3.80
S: 26.9 – 27.5
P: 3.59 – 3.40
N: 591
44.3-9.8-45.7
G: 3.69 – 3.90
S: 26.2 – 27.4
P: 3.63 – 3.25
N: 1116
30.3-13.0-56.6
G: 3.14 – 4.27
S: 23.0 – 29.9
P: 3.94 – 2.98
N: 738
45.8-12.2-41.8
G: 4.00 – 3.84
S: 27.3 – 26.9
P: 3.48 – 3.51
N: 716
Forecheck 40.6-11.3-47.9
G: 3.63 – 4.12
S: 26.7 – 28.0
P: 3.73 – 3.29
N: 2816
43.1-11.6-45.1
G: 3.80 – 3.93
S: 27.1 – 27.3
P: 3.51 – 3.54
N: 915
27.8-11.3-60.7
G: 3.06 – 4.62
S: 22.1 – 30.4
P: 4.11 – 2.89
N: 1801
55.2-9.2-35.5
G: 4.24 – 3.32
S: 29.8 – 23.4
P: 3.23 – 3.77
N: 1343
41.8-12.2-45.8
G: 3.84 – 4.00
S: 26.9 – 27.3
P: 3.51 – 3.48
N: 716

The results have been compiled from games played in some German, Slovak, Czech and Latvian leagues in game days 22 through 38 (the games with participation of noname teams have been excluded), so all from the second round. Hence, this summary does not include any games used in this previous study.

We see that even though the teams have developed, the tactics still work in a very similar way, in particular, the ring of countertactics Normal > Defensive > Offensive > Forechecking > Counterattacks > Breaking up > Normal remains valid.

My last words for today: I am looking for new ideas. If you want a certain aspect of this game being dissected in a similar manner, please contact me or drop a line in the comments and I’ll see what I can do.

Good luck in the upcoming play-offs, folks!

Legal disclaimer: Dear guest who might have stumbled at this site and wonder what it is all about, please be aware that you are reading and using this ressource at your own risk. I won’t be liable for any kind of damage, whether direct or indirect, resulting from use of the information provided in this site, including but not limited to screwing up vitally important games and getting a round-house kick from Mr. Chuck Norris after having advised him to use this site. I am just a regular user of PPM and have no connections to the development team, however, I do assume that the game engine will change with time and this information will eventually be out of date. In such cases I am under no obligation to update this information. Ich habe fertig.

July 13, 2009

1st season: 3/4 completed

Filed under: GVE chronicles — glanvalleyeaglets @ 7:56 am

With 2 games left to go in the regular season lets take a look back at the past 4 weeks.

League games. Trying to break the curse of Counterattacks vs Breaking, Eaglets set out with normal game importance and miserably failed to convert the many chances they got. Penalty minutes 0:14, shots 43:22, final score 1:1.
Ulm’s keeper Denis Jahns was amazing in the 65 mins. Two days later V.I.P. was downed in a defensive battle – 5:0, however, the next game day saw the next defeat – a 4:5 in an away game against the underdog O-Town City. The Eaglets had shown their dislike for destructive tactics again, failing to beat Normal with Breaking up.

The situation was saved by a few stellar games in a row – 7:2 against Ludwigshafener Freezers, 5:1 over Ortlfinger EV, 8:2 over EC Crossover and, finally, a 9:0 against Blue Lions in an away game. The winning streak of offensive tactics coupled with low importance ended abruptly against another underdog – 2:3 against Eisbären Dielbach. Our top goalie Florin had caught a flu and two other first team players were injured, but that’s no excuse according to the manager in chief. Nevertheless he put the first goalie back in net even though he was not fully recovered (and the goaltending in team strength section dropped from 16 stars to 14…). The opponent was no-one less than ESV Kaiserslautern Koi´s in front of 998 spectators! Playing offensive against counterattacks, the Eaglets equalized the score thrice, but in the last minute of the regular time made the deciding mistake – the game ended with a 3:4 defeat and Koi’s took over the leading spot in the league table. As if this hadn’t been enough, Briesnitzer Eisballerinas managed to beat us again, 4:3.

A losing streak of 3 games is not the best way to face the play-offs, the only bright side of that is – we have been warned! The plan is simple now – to secure the 2nd place in the league and show the best in the last and hottest phase of the season.

National Cup. Our rivals in the 1/16 final were Ruhrpott Icetigers. Their last login time suggested they’d use the same tactics as in the previous league game, namely, normal and forechecking, so the Eaglets chose to play normal, attacking. The start of the game was delayed due to technical problems, so for a while I felt like a Schrödinger’s cat eagle – a superposition of dead and alive states. Luckily, the tactics worked well and after nearly 30 minutes of goalless pressure the Eaglets broke the deadlock. Capitalizing on 3 of 9 powerplay chances, Eaglets won the game with 6:1 in front of almost 600 attendants.

In the round of last 16 we faced one of the strongest teams in the pool – TowerStars from II.1. Even though TowerStars use Normal tactics for official games, we didn’t expect an easy game and actually used High game importance for the very first time. Since the rivals were rated 16 star overall at the moment (equaling my Eaglets), breaking up tactics did not seam misplaced to me. Actually the tactics worked out quite well, leading to a shot differential 14:5 after the first period. Unfortunately the goalie Florin had a bad day and could not stop two of the five shots. Our forwards kept silent – 0:2. Uli Nock came for the unlucky Florin and in 42nd minute Jacobsen equalized – 2:2, however, soon after another blunder TowerStars took the lead again. Uli Nock had to leave his place for another forward and Andi Jacobsen scored again – 3:3. A pretty quite overtime followed where the teams hardly got scoring chances. In the penalty shoot-out, the captain Jonathan Schirmer missed three chances, Rafael Grundmann two and Mojmír Vávra one chance, however, Uli Nock kept the Eaglets in the game, stopping first 5 attempts. However, the 6th was the last. 3:4 SO ends our dreams of international cup games in the next season… TowerStars have reached the finals after beating the very strong Düsseldorf Allstars in semis, 2:6! So tomorrow is the final – Bodensee Devils (III.4) vs TowerStars (II.1).

Libertadores Cup has reached the final stages as well. Having eliminated HK Valmet in a best-of-three series 2:1 (2:3, 6:4, 5:4) and the finalist of Latvian NC – RTM Tigers with 2:0 (4:2, 6:5 OT) (gee, why can’t we play like this in official games!?), the Eaglets face LionTeam in semis.

Development: TF 5(6), REG 5, HRED 4, SA 6. Yep, the best OR of a player from SA was 171… No, it isn’t the world yet, is it?

Current team rating: 96.85 making us 157. in the world and 8. in Germany. The best position ever was reached on 2.07.09, #44 WR, #2 DE. Team strength: GK 16, DF 17, OF 17, SH 16

Coming next: a big update on the stats is planned after the regular season.

Yep, take care anybody who reads this, best of luck and have fun!

July 9, 2009

Game importance revisited

Filed under: PPM.statistics — glanvalleyeaglets @ 8:02 am

In a previous posting I had published some data that suggest a very minor impact of game importance on the final result. There was this feeling that playing with higher importance was as “D&G” – expensive and stupid (Dorogo & Glupo as they say). Now this is not the whole truth. Taking a bigger sample of match reports from League match days 1 through 4, this is what came out.

Low vs Normal
Sample size: 700 games
Result in percents: 41.1 – 11.2 – 47.5
Average result (goals): 3.71 – 3.96
Average shots per game: 25.4 – 28.9
Average penalties in minutes: 2.54 – 4.44

Low vs High
Sample size: 88 games
Result in percents: 35.2 – 10.2 – 54.5
Average result (goals): 3.15 – 4.07
Average shots: 22.4 – 30.0
Average penalties in minutes: 2.65 – 4.36

Normal vs High
Sample size: 1978 games
Result in percents: 41.9 – 14.0 – 44.0
Average result (goals): 3.88 – 3.89
Average shots: 26.2 – 28.5
Average penalties in minutes: 2.67 – 4.35

So we see that the team playing the higher importance has a slight but firm advantage in the games in spite of more time spent in the cooler. In a sharp contrast to this, the following are results from the second round (match days 22 thru 35).

Low vs Normal
Sample size: 2649 games
Result in percents: 51.0 – 12.1 – 36.7
Average result (goals): 4.00 – 3.42
Average shots per game: 26.6 – 27.4
Average penalties in minutes: 2.43 – 4.61

Low vs High
Sample size: 582 games
Result in percents: 74.3 – 7.2 – 18.3
Average result (goals): 5.42 – 2.56
Average shots per game: 28.9 – 24.1
Average penalties in minutes: 1.85 – 5.12

Normal vs High
Sample size: 5188 games
Result in percents: 71.4 – 8.4 – 20.0
Average result (goals): 5.27 – 2.69
Average shots per game: 29.7 – 23.2
Average penalties in minutes: 2.14 – 4.84

Actually that’s it for today. The figures here seem to say more than thousand words. Ok, I’ll write the essence of this all anyway: High importance should be used only a few times in a season. Misuse shall be punished by the game engine.

June 15, 2009

The next moon

Filed under: GVE chronicles — glanvalleyeaglets @ 9:29 am

The river of time keeps flowing and the time has come for the second monthly progress report of my team. Moon is my unit of time that lasts four calender weeks, thus a PPM season can be conveniently divided into 4 Moons and four reports. The pain of writing the report gives a new meaning to the word Moonsorrow 🙂

I have slightly extended the range of data I’m collecting for statistics, including shot efficiency, penalty minutes and some rudimentary statistics on the team strength “stars” (the numbers estimating the overall goaltending, defence, offence and shooting abilities). Measuring the effects of these stars is a very elusive stuff, I must say. It will take some time to collect enough of data worth of publishing, but some day you can hope to find an executive summary in this blog.

Let us turn to the performance of my own team.

The second moon has seen 12 rounds of regular season league games. This time my Eaglets had to face all three Noname teams that currently reside in our German league III.7. I will focus on the remaining 9 games, which ended with 4 victories after regular time, 3 overtimes (1 won, 1 lost, 1 draw) and 2 losses. Perhaps we have lost some points due to the low game importance, but I have to admit that most of the points were lost after a tactical defeat. Sure, it is nice to win, but the game wins in substance when the other side uses unexpected tactical variants 🙂

Having started with two victories 6:2 and 8:1, the Eaglets faced the leading team of the league in terms of facility development, ESV Kaiserslautern Koi’s. With an aggressive transfer policy they have erected the 6th level of TF and 5th level of RF and HRED. I think we’ll see this team go really far indeed. The derby in front of 429 we played defensively against breaking tactics. An exchange of many goals ended with zero points for the Eaglets, 6:7. Our next guest was the 17th number of the league who reached the round of last 64 in the National Cup – Briesnitzer Eisballerinas. Although Eaglets had the clear tactical edge defensive vs offensive, the only reward was one point – 4:4. There followed another victory 7:3 and an unexpected loss against Crusaders’ top goalie Kannast with 1:3. The game aganist Darmstadt improved the situation – 4:1, however, the last two games ended in overtime. First the Eaglets managed to take 2 points against Iron Wolves with 5:4 in OT, but on Friday we lost against Berlin Capitals 6:7 OT (reminding of the match in the first round). The strange thing is that Eaglets keep flying on Position 1 in the table, 3 points ahead of Koi’s and 11 points ahead of the 9th placed Capitals. The margins are still extremely narrow and the competition on the verge of play-offs is extremely tough. With 14 rounds remaining, the 11 point margin lets us feel everything but safe.

The hunting grounds of the National Cup are considered to be even more rough and dangerous as the first slip is usually the last. It took both skill and fortune for the Eaglets to reach the round of last 32. The National Cup, probably the highest peak a team can reach in the first season, is only five games away. So close at hand, yet as unreachable as the Moon 🙂 The first serious opponent was Red Devils Berlin. Eaglets played offensive against forechecking and marked an impressive 8:2 victory. The next in line was PreussenSpiders with an inactive manager. Scoring 2 goals in the last seconds, Eaglets saved the day with a tough 4:1 victory. HC Growl fought furiously using the forechecking tactics against offensive finding a response to every goal scored by the Eaglets. As the evening dusk fell, we saw a deserved yet lucky 6:5. The next draw revealed SRBIJA as the next opponent, but they went anonymous right before the match. Although they, having been overtaken by a new manager in the last minutes before 18.00, did show a serious resistance, Eaglets still managed to convert this chance – 6:2 and here we are.

Ruhrpott Icetigers from IV.1 will be the next team that comes to Angelo d’Arrigo Ice Forum with the firm resolution to kick the hosts out of the tournament. In terms of stars the Eaglets have a slight but firm advantage, yet the manager of the Icetigers cares well for his team, so the tactical schemes are adapted on a regular basis. I’m flirting with the idea to send my Eaglets in the fight with high importance for the very first time. Unfortunately, I have some good arguments both pro and contra, so it is going to be a tough last minute decision for me.

Sometimes I wish I could transfer the success streak in friendlies to the league. In the last 5 weeks Eaglets have won 15 of 15 friendlies, thus winning the Group D in Libertadores Cup with a considerable margin. Now we can prepare for the play-off rounds in series of best of 3. Hoping for more than just one round.

Players. Still waiting for the first blue wonder from our Sports Academy. We already have 5th level for a while, and still waiting for the first youth player to come with an OR over 160. The only serious addition to the roster was the defender Rainer Buhs. The french goalie Gary Florin was the second addition to the team. There have been no big transfers to report.

Facilities: HR 4, REG 4, TF 5, SA: 5 (6). Team strength (a.k.a. Stars): GT 15, DF 16, OF 16, SH 15 (16). Overall team rating 69.49. Place 90 in a out-of-date world ranking table (there are over 25 000 active teams around) thanks to the lucky streak in the National Cup, and place 2 in German ratings (out of date as well).

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