Summary: NHL Eastside Hockey Manager 2007 is the follow-up to the game that captured Jakub the Canuck's heart last year. What kinds of improvements does it deliver? Find out!
The most noteable change in the game is the brand-new simulation engine. The top-down 2D display of the hockey games themselves is much advanced over its predecessor. Players are no longer stuck in moving between fixed positions on the map of the rink, but flow smoothly along the ice. The game can be viewed in speeds as slow as 0.25x real-time to a fast forward mode where the standard 20-minute period takes less about thirty seconds. Passes, hits, poke checks, and shots are all visible. In general the new simulator does a good job of presenting the game, though it’s not perfect.
At the NHL level, a game averages about 30 shots on goal per side, and probably another 30-50 attempted shots that are blocked, flubbed, or miss the net. Rarely will a team have more than 40 or fewer than 20 shots, or less than 10 or more than 20 in a period. Eastside Hockey Manager’s simulation engine is somewhat more eccentric, with teams being able to manage over 40 and under 25 shots with a fair amount of regularity. Occasionally, this is reflected in the scores, we’ve seen some as absurd as 13-0, though in general the game does limit itself over the course of the season to a believable per-game average. One area where there seems to be a discrepancy is in scoring assists; it is far too common to see a player with more goals than assists, which is a rare event in the NHL.
Notably, the game is not all about the NHL. The OHL, QMJHL, WHL, AHL and Swedish Elite League, Deutsche Elite League and others are available. These are the licensed ones, others are without license but contain the players and teams. The only time where real player names are not available are, of course, for NCAA teams, which provide only a minority of the players in the draft for hockey.
Despite these flaws, the new engine is a great advancement over its predecessor. Changes in tactics are more visible. The drawbacks of having a slow team are painfully apparent as opposing skaters, even if weaker and less skilled, blow by your defensemen. Unfortunately, the manual is not very handy in suggesting the proper ways to exploit your player’s skills, but with some fiddling around you can get a vague idea of what works best.
In fact, the tactical options are a considerable blessing. You can dictate how creative passing is, what kind of tempo your players try to maintain, how hard they hit, how aggressively they backcheck, what kind of forecheck strategy your lines use, what formations, and even where they shoot. The shot targeting can be as specific as choosing the high glove or stick side of the opposing goalie. If you face, for example, Tomas Vokoun of the Nashville Predators, who is rated at only 1 on his stick side, you will almost certainly want to direct your players to aim there.
Then again, specific coaching details can be left to the AI coach. The coaches vary in quality like players, and also like players, aren’t necessarily as effective as their stats would indicate. The presence of hidden stats like “current ability” and “potential ability” can drastically affect a player’s or coach’s current and future performance, even though his visible statistics may look great or mediocre. For players, you can get a sense of how good they are by scouting them repeatedly, but there is no such option with coaches, assistant GMs, and scouts.
Of course, like real hockey, scouting isn’t perfect. It pays off significantly to scout players repeatedly, as their value may go up and down. You may draft an early 1st-round bust like Alexander Daigle but pick up a 4th-round gem such as Mark Recchi. As painful as this may seem, we’ve found that an attentive human player can usually draft much better than the AI. Of course, the top picks almost always pan out in one form or another, but whether or not they become superstars as projected is never guaranteed. In one game as the Oilers, a defenseman I drafted in the third round ended up winning the Norris Trophy eight years later.
Player growth and development is merely reflected in their attributes, skills and stats. The first two are rated from 1-20, while stats are of course the living examples of their performance on the ice. One area where EHM could improve is to show the physical change in players. You’ll find yourself, for example, drafting 6’5”, 229lb players… who are 17 years old. They never grow, never gain or lose weight, and are remarkably static. We’re not sure if these physical traits have an effect on the game, but it’d be a nice touch of realism to see that scrawny 17-year-old potential superstar fill out his frame. Similarly, you get odd statistics for a player’s size every now and then. You’ll find 5’10”, 170lb centers with high strength, for example.
The game’s AI is very, very good. It is easier to talk about the few points where it makes mistakes, rather than writing up a long list of its strengths. However, for the sake of balance… the AI is really remarkable. It values players very well based on their current ability and potential. It is especially unforgiving in trades that involve draft picks, and it commands a high price for its superstars. When the Anaheim Ducks soured on Scott Niedermayer and had him rated at 4 stars rather than his usual five (or even “UNT”, meaning Untradeable), they still expected nothing less than my two best prospects, a first round pick, and a very good second-line left winger. Ironically enough, this is almost the exact same kind of trade the Edmonton Oilers got in return for Chris Pronger when he demanded out of town in real life. The AI is also better about contracts than it was in EHM 2005, getting into cap trouble less often and being able to get out of it easier. Of course, every now and then, it’s possible to fleece the AI in a trade. I got Jay Bouwmeester for a 2nd-round draft pick and a defenseman of mine the Florida AI had seriously over-rated (sorry, Steve Staios). It is nice, however, that a player on a hot streak or one that benefits from being on a line with a lot of chemistry can be traded for a lot more than he’s worth (or that you think he’s worth).
Where the game’s artificial intelligence fails is in waivers. Surprisingly often, it will give up second- and third-line quality players for nothing simply by putting them on waivers for the player or other AI GMs to pick up. It is weak at offering trades to the player, though there is, if anything, an over-abundance of roster moves. Blockbuster deals are somewhat common though not abusively so, however, major supporting cast players, as well as the older, or the ones with expiring contracts, get moved around with alarming frequency. Michael Peca, Scott Hannan, Eric Brewer, Sergei Fedorov, Pavol Demitra, Lubomir Visnovsky, Brendan Shanahan, Petr Sykora, Robyn Regehr, Martin Biron, Alex Auld, and Martin St. Louis are noteable figures to have moved around a lot in many of my games.
A few bugs
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