Clickpitch: birth of real video games

 Posted by on May 3, 2011 at 13:28  applications, internet, mobile, sports, startups, telepresence, video calling  1 Response »
May 032011
 

The hitter’s knees buckle as he can only watch a filthy slider catch the inside corner for strike two. The pitcher considers his next pitch choice, from the other side of the world, enabled by Clickpitch.

Video game baseball with physical hitters and actual pitched balls
The hitter digs in for the next pitch in a batting cage type facility loaded with sensors, video cameras and software that virtually transform the cage into a full-size field. 60 feet, 6 inches from the hitter is a sophisticated pitching machine, or even a robot pitcher, but the brain of the pitcher can be anywhere – the machine is told what pitches to throw from anywhere on the web, as part of sophisticated video games and training programs.

In this case, a boy in Japan, via his Droid, is considering following up the slider with an inside, shoulder high, 86 MPH fastball, a couple inches off the inside corner, trying to get the hitter to chase for strike three, although his tweet stream and GroupMe group is urging him to go back to the nasty slider. The pitcher could just as easily be an actual MLB starting pitcher, playing on his iPad in the clubhouse on his day off, and for that matter the hitter could be an MLB hitter getting his practice in.

Pitching from Android, Wii, Xbox, iPad
The boy in Japan is not just any pitcher – he is a superstar – he leads both the Facebook and MLB.com virtual Cy Young award voting for Clickpitch enabled baseball video games. If he still leads at the MLB all-star break, he will pitch an inning to the National League All-Star team at Citi Field, using his Droid. He’s a catcher on his school team so knows a thing or two about pitch selection.

The robot pitchers can emulate all levels from a Little League pitcher to a MLB ace, including software to manage margin for error by level, e.g. if the bot is a Little League pitcher instructed to throw an outside corner fastball then he might hit the hitter, whereas the MLB ace robot pitcher is going to paint the black. Actual MLB pitchers, based on their actual pitch data, can be imitated such that a hitter can choose to face Roy Halladay in the first inning, Clayton Kershaw in the second and CC Sabathia in the next. Today’s advanced statistics, pitching charts and sabermetrics could make this very sophisticated.

Hitting against smart pitchers instead of dumb machines
The hitter is enjoying the best offseason hitting practice of his life, as he’s now facing pitchers that are trying to get him out, based on his strengths and weaknesses, and the pitchers’ characteristics. The session are not lost when the hitter leaves the virtual field – the hitter uses telepresence to work with his coach at anytime and video to keep all results. Right now, the coach, from his basement office, demonstrates over telepresence a swing change that he wants the hitter to try. Video is tagged such that the hitter and his coach review all clips of swings on 90 MPH fastballs on the inner half of the plate over the past two months, or any other set of swings they want to analyze. A former coach that is in the hitter’s GroupMe baseball group may contribute advice about a subtle change in stance that he’s noticed in the hitter over time.

The next generation of baseball video games and video game ecosystems
Clickpitch turned legacy baseball video games into typewriters – most people barely remember them. There are hundreds of different video games on various platforms that simultaneously utilize each at-bat between hitter, robot pitcher and pitcher controller, creating millions of parallel games for players to join at anytime. Some video games for example create entire games for their users, enabling broadcasting students to call each game, whereas others are geared purely towards training and practice.

Little League numbers have soared as well as America’s pastime has been reinvigorated by kids being introduced to baseball on their iPad apps, going to the hitting zones to compete against their friends and then finding their way to their local Little League programs.

Clickpitch is the software and algorithms platform, built in the new peer-produced, crowdsourced product development model. Many companies have leveraged Clickpitch data and APIs to add various sensors, telepresence solutions, video games, statistical packages, iPhone and iPad apps, browser-based games, robots, pitching machines, video footage review products, social net integrations, etc. Some college coaches run full live practices but with the pitcher replaced by the Clickpitch/bot/virtual pitcher combination. In this way, pitchers’ arms are saved from practice innings, while the hitters still face top quality pitching and all the statistics and video clips are archived away for follow-up.

Note: ClickPitch doesn’t exist. I offer the idea out to the interwebs in the event that someone wants to run with parts of the idea. Meanwhile that boy in Japan is anxiously waiting for the opportunity to pitch to David Wright at a future MLB All-Star game.

    Facebook is dead. Long live Facebook.

     Posted by on January 17, 2011 at 22:02  internet, startups  1 Response »
    Jan 172011
     

    The inflection point at which large companies start to flat line is very close in time to the point where their revenue goals drive their roadmaps more than their users drive their roadmaps.  Facebook is there.

    FB is now a huge company. To grow even small percentages annually will require massive absolute growth.  For FB, massive growth means mainly enabling more third parties to have better and deeper ways to monetize more Facebook users.  That grows revenue but won’t always provide more value to Facebook users.

    • When talented FB engineers work on a revenue-driven initiative, what’s the opportunity cost?  Many features that provide users with great value don’t start that way – they iterate.  If the grandfather of feature x isn’t released now then the killer feature grandson will be birthed elsewhere.
    • When FB makes changes or adds feature for revenue, how many of those will annoy me from user perspective? How muddy does the FB water become over time? We’ve seen FB do this already but so far they’ve survived each storm of user protest – but very few, no matter how strong, can weather too many of these storms.
    • How many times will dirty words like self-cannibalization and competing interests enter design discussions where they don’t belong?  Big companies like Amazon have brilliantly avoided this for the most part – but that will be harder for FB with their revenue reliance on third parties and advertising.
    • On a micro-level, would FB have done an Instagram two years ago?  On a macro level, should FB have a mobile platform that’s in the ballpark with Android and iOS?
    • How many things would FB have done without a second thought as a small startup that they wouldn’t be willing to do as a large company with more hierarchy, bureaucracy and legal considerations? 

    FB won our social graph by being the best for basic online communication, social discovery and media sharing. FB hit those use cases within a simple, powerful, engaging, interactive, social user experience.  Not easy and FB crushed it, and as a result now own our social graph.  But the world changes:

    • How many good choices are there now for those use cases?  How many of them are in your pocket on your phone?
    • How much of what used to be done on Facebook is now done on Twitter, Instagram and mobile IM?  How much will be done on GroupMe, Beluga and The Next Big Thing? 
    • How different is what you would have done on Facebook four years ago to what you’d do now with the amount of eyeballs and APIs exposing that data to third parties?  How will FB competitors take advantage of that?

    Facebook does own our social graph, but even that is under pressure as we really have social graphs, plural, and will demand control of each.  FB could potentially innovate in key adjacent areas such as mobile, virtual goods, gaming, analytics and location.  They obviously have very talented employees, good processes and an excellent foundation.  They should have some funds to buy external innovation. But I think Facebook – the original Facebook juggernaut – is dead.  If Facebook is to survive, they’ll evolve to a different company than the one we know today. Not many big companies pull that off.

    Disclaimer: I pronounced Facebook dead a few years ago too, and being wrong then was one reason why my startup failed (more startup learnings here), so maybe I should move on to being wrong about something new. 

      lessons for entrepreneurs

       Posted by on January 5, 2011 at 23:36  startups  3 Responses »
      Jan 052011
       

      My learnings are unfortunately from a failed startup, LocalReplay, but you learn more after you fail (while you are failing, you are still seeing the world the way you want to see it).

      Lesson #1: you don’t learn anything that your customers don’t teach youspeed-learning-focus
      Go live and iterate. Rapidly. Now. Eliminate long development cycles – you’re not learning while you are in a long dev cycle. If you are not learning, you are going backwards. Iterate rapidly, quickly analyze customer usage, measure, subtract, add, measure, build. Quickly – your analytical and predictive capabilities are not as good as you think – more important to move as only by moving will you really find the path.

      Our successes at LocalReplay came when we made changes based on user feedback (direct and observed), but we were doing too much analyzing and not enough iterating, so we were moving too slowly.

      This doesn’t mean only build what customers ask for – they don’t know the future and think of extensions more than disruptions – build your dream, build your disruption, but do it in small, quick, measurable steps.  Update: came across this great graph from Ash Maurya which demonstrates this well:

      Amazon, from the outside, seems to do this very well.

      Lesson #2, “system functionality” is an oxymoron; UX is everything
      At LocalReplay, we too often prioritized functionality over usability. System functionality doesn’t mean anything – users define actual functionality – what they perceive, how they use the system, how they don’t use it. Put expertise, time and dollars into UX. It is a science. It is an art. It is absolutely critical, especially in today’s ADD society with our low signal to noise ratios. It is there but users haven’t started to use it yet? Then it isn’t there and doesn’t matter.  

      Many Google products are good examples of hyperfocus on usability – consider the simplicity of Google’s search page compared to Yahoo’s – and that was before the overwhelming noise of today’s web.

      Lesson 3, KISS, hyperfocus, don’t get distracted
      We had a KISS strategy – we’d succeed in one type of community in one local market – and then expand. But we didn’t stick to it – we had some growth in other areas and lost focus on our initial set of simple goals. We started building cities before we really nailed the first building.  Ironically, our belief was that the big social networks – mainly Facebook – were too big and too horizontal and more focused startups could provide users with more value in specific verticals.  And yet we didn’t KISS – we immediately made our world more complex than it needed to be. Hyperfocus is an advantage of being startup, one of the few you’ll have, don’t let it escape.

      Lesson #4, forget about going viral
      Lots of things can succeed if they go viral. The fact that your plan works if you can get network effect doesn’t mean much. Learning how to succeed on a smaller scale is invaluable and you won’t do it if your plan is built on going viral, including goals, cost management and design decisions.

      Lesson #5, move to plan B based on pre-set criteria
      We had a plan B, multiple plan Bs, but never pulled the trigger. You are partially blind when doing a startup so you may see a red flag as a small bump in the road (or rationalize it that way). You’ll make changes to deal with the bumps, but will stay on that street. You need to establish some pre-set markers to force you to get off that street when it is necessary. You don’t need to abandon the course – you may well come back to it – and you may still get to the same destination.  Twitter may be the best current example.  I don’t know their evolution track, but I know their initial plans were nothing like what evolved.

      Lesson #6, go with your gut
      Not enough information to make the decision? Welcome to start-ups. Keep moving – don’t use too many cycles for individual decisions. You’ll make lots of good decisions and many bad decisions but the sum of your decisions will be positive as long as you keep moving and keep learning.

        car stereo + internet

         Posted by on December 16, 2010 at 15:55  internet  1 Response »
        Dec 162010
         

        car-stereoI want a car stereo with a mobile broadband connection, receiver and a separate interface to customize my car audio experience from the web – customized algorithmically but with a layer of individual curation to bound the algorithms.

        Traffic and weather “channels” will push content (broadcast signal or web streaming) to me based on my location (mobile triangulation is precise enough or GPS can provide) and future location (partially based on the Google Maps query I did last night). Algorithms will push “station one” to me if it is 8:00 and that station does traffic at the top of the hour, or push station two if their time better matches, or push the most recent cached update if none matches, or push me the station with the highest ratings for traffic. When Twitter and similar data is better curated, I can listen to tweets about an accident that just happened at an intersection that I’m due to hit in ten minutes.

        My favorite music will be pulled in from Internet radio services like Pandora. If I want to surf randomly, but based on my preferences, other stations will pull in local broadcast stations that meet my criteria. Programmed, smart “seek”. Same for sports, news, weather etc. Similar model for all other content in the cloud – podcasts, audio books, MP3s etc. Ditto for other content that I may want in audio form – voicemails, texts converted to voice, etc.

        The algorithms that build my channels will incorporate social networking and social graph, recommendation engines and crowdsourcing, along with my preferences and curation. Channels that feature content that is most in play in my social network. Or least in play if that’s my persuasion. Brand new content that I might not know about but is recommended based on my preferences, listening history and social graph. Etc. When my wife drives my car, she switches to her profile – as long as I’m not a passenger ; ). And that’s just from a non-professional driver’s perspective – there are more interesting use cases for professional drivers, from trucking to FedEx to taxis.

        I think we’ll see this model of individual customized pushes of slices of content in many other areas too, e.g. TV, as a specific type of signal to noise solution via web services, but think specific use cases like car audio experience could be the first to develop with the least barriers in the way and the most opportunity for the various players.